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    <title>Pinboard (cshalizi)</title>
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    <description>recent bookmarks from cshalizi</description>
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	<rdf:li rdf:resource="https://journals.sagepub.com/doi/10.1177/00104140241271104"/>
	<rdf:li rdf:resource="https://arxiv.org/abs/2503.04020"/>
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	<rdf:li rdf:resource="https://jamanetwork.com/journals/jamapsychiatry/fullarticle/2818735"/>
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	<rdf:li rdf:resource="https://arxiv.org/abs/0812.1435"/>
	<rdf:li rdf:resource="https://papers.andycao.org/Media_Crime_Project.pdf"/>
	<rdf:li rdf:resource="https://www.journals.uchicago.edu/doi/10.1086/427320"/>
	<rdf:li rdf:resource="https://www.science.org/doi/10.1126/sciadv.abe5641"/>
	<rdf:li rdf:resource="https://www.cambridge.org/core/journals/british-journal-of-political-science/article/interaction-of-structural-factors-and-diffusion-in-social-unrest-evidence-from-the-swing-riots/A47B26A2C4B4A196207181DC7737627E"/>
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	<rdf:li rdf:resource="https://journals.sagepub.com/doi/abs/10.1177/00491241211014237"/>
	<rdf:li rdf:resource="https://arxiv.org/abs/2105.05662"/>
	<rdf:li rdf:resource="https://doi.org/10.1093/sf/sou101"/>
	<rdf:li rdf:resource="https://arxiv.org/abs/2104.10365"/>
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	<rdf:li rdf:resource="https://arxiv.org/abs/1812.02276"/>
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	<rdf:li rdf:resource="https://arxiv.org/abs/2102.02382"/>
	<rdf:li rdf:resource="https://journals.aps.org/pre/abstract/10.1103/PhysRevE.103.022303"/>
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	<rdf:li rdf:resource="https://science.sciencemag.org/content/371/6525/153"/>
	<rdf:li rdf:resource="https://arcdigital.media/no-professors-are-not-brainwashing-their-students-d4694522f413"/>
	<rdf:li rdf:resource="https://arxiv.org/abs/2012.12309"/>
	<rdf:li rdf:resource="https://osf.io/preprints/socarxiv/ec46t/"/>
	<rdf:li rdf:resource="https://arxiv.org/abs/2012.08925"/>
	<rdf:li rdf:resource="https://arxiv.org/abs/2012.08572"/>
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	<rdf:li rdf:resource="https://sociologicalscience.com/articles-v7-18-433/"/>
	<rdf:li rdf:resource="https://www.kayladelahaye.net/outcomes2"/>
	<rdf:li rdf:resource="https://arxiv.org/abs/2011.15083"/>
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	<rdf:li rdf:resource="https://iopscience.iop.org/article/10.1088/1742-5468/2013/12/P12002"/>
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	<rdf:li rdf:resource="https://arxiv.org/abs/1908.10129"/>
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	<rdf:li rdf:resource="https://arxiv.org/abs/1906.09076"/>
	<rdf:li rdf:resource="https://arxiv.org/abs/1906.08772"/>
	<rdf:li rdf:resource="https://www.tandfonline.com/doi/full/10.1080/01621459.2019.1617153"/>
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	<rdf:li rdf:resource="https://journals.sagepub.com/doi/10.1177/0081175018820075"/>
	<rdf:li rdf:resource="https://onlinelibrary.wiley.com/doi/book/10.1002/9781118833162"/>
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	<rdf:li rdf:resource="https://arxiv.org/abs/1811.10372"/>
	<rdf:li rdf:resource="https://arxiv.org/abs/1809.10302"/>
	<rdf:li rdf:resource="https://www.aeaweb.org/articles?id=10.1257/aer.20141708"/>
	<rdf:li rdf:resource="https://press.princeton.edu/titles/11279.html"/>
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  </channel><item rdf:about="https://arachnemag.substack.com/p/the-case-against-social-media-is">
    <title>The Case Against Social Media is Stronger Than You Think</title>
    <dc:date>2025-09-15T16:11:08+00:00</dc:date>
    <link>https://arachnemag.substack.com/p/the-case-against-social-media-is</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>networked_life social_media social_influence re:actually-dr-internet-is-the-name-of-the-monsters-creator have_read via:henry_farrell us_politics social_science_methodology</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:5bcd4ef0751c/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:networked_life"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_media"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_influence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:actually-dr-internet-is-the-name-of-the-monsters-creator"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:henry_farrell"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:us_politics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_science_methodology"/>
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<item rdf:about="https://journals.sagepub.com/doi/10.1177/00104140241271104">
    <title>Separated by Degrees: Social Closure by Education Levels Strengthens Contemporary Political Divides - Jona de Jong, Jonne Kamphorst, 2025</title>
    <dc:date>2025-04-28T01:26:35+00:00</dc:date>
    <link>https://journals.sagepub.com/doi/10.1177/00104140241271104</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Across Western democracies, education levels are predictive of immigration attitudes and voting for new left or far right parties. What explains education-based political divides? This article proposes that social closure of higher- and lower-educated citizens strengthens and reinforces differences in political attitudes and voting between them. Using social network data from the Netherlands, and ESS data, we show that large proportions of higher- and lower-educated citizens report no close relationships with different education levels. Network education levels, in turn, are predictive of immigration attitudes and voting behaviour. Difference-in-differences models show that a change in network education levels is associated with change in these outcomes. Our findings contribute to literatures on educational divides and peer effects. Moreover, they support an interpretation of political competition on the universalist-particularist dimension as durably rooted in social structure. Sizable, distinct and insulated educational groups can crystallize contemporary divides and predictably shape political reality."

--- Very curious to see the difference-in-difference argument.]]></description>
<dc:subject>to:NB political_science education social_networks polarization homophily social_influence to_teach:statistics_of_inequality_and_discrimination</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:9bb0db51ef6b/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:political_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:education"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:polarization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:homophily"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_influence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:statistics_of_inequality_and_discrimination"/>
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</item>
<item rdf:about="https://arxiv.org/abs/2503.04020">
    <title>[2503.04020] An Approximate-Master-Equation Formulation of the Watts Threshold Model on Hypergraphs</title>
    <dc:date>2025-04-09T14:12:55+00:00</dc:date>
    <link>https://arxiv.org/abs/2503.04020</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["In traditional models of behavioral or opinion dynamics on social networks, researchers suppose that all interactions occur between pairs of individuals. However, in reality, social interactions also occur in groups of three or more individuals. A common way to incorporate such polyadic interactions is to study dynamical processes on hypergraphs. In a hypergraph, interactions can occur between any number of the individuals in a network. The Watts threshold model (WTM) is a well-known model of a simplistic social spreading process. Very recently, Chen et al. extended the WTM from dyadic networks (i.e., graphs) to polyadic networks (i.e., hypergraphs). In the present paper, we extend their discrete-time model to continuous time using approximate master equations (AMEs). By using AMEs, we are able to model the system with very high accuracy. We then reduce the high-dimensional AME system to a system of three coupled differential equations without any detectable loss of accuracy. This much lower-dimensional system is more computationally efficient to solve numerically and is also easier to interpret. We linearize the reduced AME system and calculate a cascade condition, which allows us to determine when a large spreading event occurs. We then apply our model to a social contact network of a French primary school and to a hypergraph of computer-science coauthorships. We find that the AME system is accurate in modelling the polyadic WTM on these empirical networks; however, we expect that future work that incorporates structural correlations between nearby nodes and groups into the model for the dynamics will lead to more accurate theory for real-world networks."]]></description>
<dc:subject>to:NB social_influence stochastic_processes networks porter.mason_a. re:do-institutions-evolve</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:8a3aab250ab3/</dc:identifier>
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	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_influence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:stochastic_processes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:porter.mason_a."/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:do-institutions-evolve"/>
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<item rdf:about="https://arxiv.org/abs/2212.12041">
    <title>[2212.12041] Estimating network-mediated causal effects via principal components network regression</title>
    <dc:date>2024-12-11T16:26:00+00:00</dc:date>
    <link>https://arxiv.org/abs/2212.12041</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We develop a method to decompose causal effects on a social network into an indirect effect mediated by the network, and a direct effect independent of the social network. To handle the complexity of network structures, we assume that latent social groups act as causal mediators. We develop principal components network regression models to differentiate the social effect from the non-social effect. Fitting the regression models is as simple as principal components analysis followed by ordinary least squares estimation. We prove asymptotic theory for regression coefficients from this procedure and show that it is widely applicable, allowing for a variety of distributions on the regression errors and network edges. We carefully characterize the counterfactual assumptions necessary to use the regression models for causal inference, and show that current approaches to causal network regression may result in over-control bias. The structure of our method is very general, so that it is applicable to many types of structured data beyond social networks, such as text, areal data, psychometrics, images and omics."]]></description>
<dc:subject>to:NB social_influence to_read re:homophily_and_confounding re:community_control</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:b9f901d80d48/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_influence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:homophily_and_confounding"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:community_control"/>
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</item>
<item rdf:about="https://jamanetwork.com/journals/jamapsychiatry/fullarticle/2818735">
    <title>Transmission of Mental Disorders in Adolescent Peer Networks | Mobile Health and Telemedicine | JAMA Psychiatry | JAMA Network</title>
    <dc:date>2024-12-11T16:17:00+00:00</dc:date>
    <link>https://jamanetwork.com/journals/jamapsychiatry/fullarticle/2818735</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Importance  Previous research indicates that mental disorders may be transmitted from one individual to another within social networks. However, there is a lack of population-based epidemiologic evidence that pertains to the full range of mental disorders.
"Objective  To examine whether having classmates with a mental disorder diagnosis in the ninth grade of comprehensive school is associated with later risk of being diagnosed with a mental disorder.
"Design, Setting, and Participants  In a population-based registry study, data on all Finnish citizens born between January 1, 1985, and December 31, 1997, whose demographic, health, and school information were linked from nationwide registers were included. Cohort members were followed up from August 1 in the year they completed ninth grade (approximately aged 16 years) until a diagnosis of mental disorder, emigration, death, or December 31, 2019, whichever occurred first. Data analysis was performed from May 15, 2023, to February 8, 2024.
"Exposure  The exposure was 1 or more individuals diagnosed with a mental disorder in the same school class in the ninth grade.
"Main Outcomes and Measures  Being diagnosed with a mental disorder during follow-up.
"Results  Among the 713 809 cohort members (median age at the start of follow-up, 16.1 [IQR, 15.9-16.4] years; 50.4% were males), 47 433 had a mental disorder diagnosis by the ninth grade. Of the remaining 666 376 cohort members, 167 227 persons (25.1%) received a mental disorder diagnosis during follow-up (7.3 million person-years). A dose-response association was found, with no significant increase in later risk of 1 diagnosed classmate (HR, 1.01; 95% CI, 1.00-1.02), but a 5% increase with more than 1 diagnosed classmate (HR, 1.05; 95% CI, 1.04-1.06). The risk was not proportional over time but was highest during the first year of follow-up, showing a 9% increase for 1 diagnosed classmate (HR, 1.09; 95% CI, 1.04-1.14), and an 18% increase for more than 1 diagnosed classmate (HR, 1.18; 95% CI, 1.13-1.24). Of the examined mental disorders, the risk was greatest for mood, anxiety, and eating disorders. Increased risk was observed after adjusting for an array of parental, school-level, and area-level confounders.
"Conclusions and Relevance  The findings of this study suggest that mental disorders might be transmitted within adolescent peer networks. More research is required to elucidate the mechanisms underlying the possible transmission of mental disorders."

--- I'm sorry, but it's preposterous that children are (effectively) randomly assigned to schools and classrooms, even in a Nordic social democracy.]]></description>
<dc:subject>to:NB social_influence social_contagion re:homophily_and_confounding have_skimmed color_me_skeptical</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:4ae3bd098dca/</dc:identifier>
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	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_contagion"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:homophily_and_confounding"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_skimmed"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:color_me_skeptical"/>
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</item>
<item rdf:about="https://sociologicalscience.com/articles-v10-28-806/">
    <title>Feasible Peer Effects: Experimental Evidence for Deskmate Effects on Educational Achievement and Inequality | Sociological Science</title>
    <dc:date>2023-11-16T03:06:01+00:00</dc:date>
    <link>https://sociologicalscience.com/articles-v10-28-806/</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Schools routinely employ seating charts to influence educational outcomes. Dependable evidence for the causal effects of seating charts on students’ achievement levels and inequality, however, is scarce. We executed a large pre-registered field experiment to estimate causal peer effects on students’ test scores and grades by randomizing the seating charts of 195 classrooms (N=3,365 students). We found that neither sitting next to a deskmate with higher prior achievement nor sitting next to a female deskmate affected learning outcomes on average. However, we also found that sitting next to the highest-achieving deskmates improved the educational outcomes of the lowest-achieving students; and sitting next to the lowest-achieving deskmates lowered the educational outcomes of the highest-achieving students. Therefore, compared to random seating charts, achievement-discordant seating charts would decrease inequality; whereas achievement concordant seating charts would increase inequality. We discuss policy implications."

--- Elwert is sound, but the fact that they find the effects only for the extremes seems a bit funny.]]></description>
<dc:subject>to:NB to_read experimental_sociology social_influence elwert.felix</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:46d9be82c91f/</dc:identifier>
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	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:experimental_sociology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_influence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:elwert.felix"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2203.14223">
    <title>[2203.14223] Identifying Peer Influence in Therapeutic Communities</title>
    <dc:date>2023-11-16T03:04:55+00:00</dc:date>
    <link>https://arxiv.org/abs/2203.14223</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We investigate if there is a peer influence or role model effect on successful graduation from Therapeutic Communities (TCs). We analyze anonymized individual-level observational data from 3 TCs that kept records of written exchanges of affirmations and corrections among residents, and their precise entry and exit dates. The affirmations allow us to form peer networks, and the entry and exit dates allow us to define a causal effect of interest. We conceptualize the causal role model effect as measuring the difference in the expected outcome of a resident (ego) who can observe one of their social contacts (e.g., peers who gave affirmations), to be successful in graduating before the ego's exit vs not successfully graduating before the ego's exit. Since peer influence is usually confounded with unobserved homophily in observational data, we model the network with a latent variable model to estimate homophily and include it in the outcome equation. We provide a theoretical guarantee that the bias of our peer influence estimator decreases with sample size. Our results indicate there is an effect of peers' graduation on the graduation of residents. The magnitude of peer influence differs based on gender, race, and the definition of the role model effect. A counterfactual exercise quantifies the potential benefits of intervention of assigning a buddy to "at-risk" individuals directly on the treated resident and indirectly on their peers through network propagation."

--- OK, maybe we should have written out the more general "assume you can estimate latent locations in an arbitrary graphon at such-and-such a rate" theorem...]]></description>
<dc:subject>to:NB to_read have_skimmed network_data_analysis homophily social_influence re:community_control</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:b0381fb50e84/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
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	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:network_data_analysis"/>
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	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_influence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:community_control"/>
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</item>
<item rdf:about="https://arxiv.org/abs/0812.1435">
    <title>[0812.1435] A Diffusive Strategic Dynamics for Social Systems</title>
    <dc:date>2023-04-24T21:34:36+00:00</dc:date>
    <link>https://arxiv.org/abs/0812.1435</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We propose a model for the dynamics of a social system, which includes diffusive effects and a biased rule for spin-flips, reproducing the effect of strategic choices. This model is able to mimic some phenomena taking place during marketing or political campaigns. Using a cost function based on the Ising model defined on the typical quenched interaction environments for social systems (Erdos-Renyi graph, small-world and scale-free networks), we find, by numerical simulations, that a stable stationary state is reached, and we compare the final state to the one obtained with standard dynamics, by means of total magnetization and magnetic susceptibility. Our results show that the diffusive strategic dynamics features a critical interaction parameter strictly lower than the standard one. We discuss the relevance of our findings in social systems."]]></description>
<dc:subject>social_influence of_course_its_really_a_spin_glass cleaning_out_the_filing_cabinet_for_the_first_time_since_2005 in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:087099701a04/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_influence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:of_course_its_really_a_spin_glass"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:cleaning_out_the_filing_cabinet_for_the_first_time_since_2005"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://papers.andycao.org/Media_Crime_Project.pdf">
    <title>Does News Coverage of Hate-motivated Mass Shootings Generate More Hatred?</title>
    <dc:date>2022-12-28T18:50:59+00:00</dc:date>
    <link>https://papers.andycao.org/Media_Crime_Project.pdf</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["This paper investigates the role that the media play in promoting hatred through the news
coverage of mass shootings. I first show through observational data that the media treat mass
shootings differently depending on the motive behind the shooting. Any time a shooting targets
a specific ethnicity/race/religion/gender/etc, i.e., the shooting is hate-motivated, its news coverage differs in two respects: 1) higher coverage; 2) more focus on the shooter. I then show that
there is more public interest in hate-motivated mass shootings, based on online searching behavior. Finally, I provide suggestive evidence that, immediately following a hate-motivated mass
shooting, there is an increase in the number of hate crimes against the same victimized group.
Based on these findings and the existing literature, I hypothesize that the way hate-motivated
mass shootings are covered in the news contributes to spreading hatred. I test my hypotheses
by conducting an online information provision experiment where I manipulate how a real past
mass shooting targeting immigrants is reported in the news. In particular, I examine how, in the
United States, Democrats and Republicans, who have different ex-ante views about immigration,
react to news coverage that emphasizes the hate ideology or the identity and personal background
of the shooter. Results from the experiment show that receiving details about the shooter’s hate
ideology increases Republicans’ support for the shooter. Emphasis on the shooter’s identity and
background increases Democrats’ support for both the shooter and the shooter’s hate ideology.
The latter finding is driven by the more right-leaning individuals within the Democrat sample."

--- I am very sympathetic to the idea that these crimes, like terrorism, should be covered only minimally by the media, since a big part of the motivation in both cases is being known.  ("The propaganda of the deed" = The infamy is the point.)  But the findings described in this abstract sound very weird, hence the last tag.]]></description>
<dc:subject>to:NB violence media_effects social_influence sociology via:? color_me_skeptical</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:308125e30dd0/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:violence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:media_effects"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_influence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:sociology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:?"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:color_me_skeptical"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.journals.uchicago.edu/doi/10.1086/427320">
    <title>Sociology and Simulation: Statistical and Qualitative Cross-Validation | American Journal of Sociology: Vol 110, No 4</title>
    <dc:date>2022-04-13T12:37:38+00:00</dc:date>
    <link>https://www.journals.uchicago.edu/doi/10.1086/427320</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Agent‐based simulation modeling enables the construction of formal models that simultaneously can be microvalidated against accounts of individual behavior and macrovalidated against aggregate data that show the characteristics of many socially derived time series. These characteristics (leptokurtosis and clustered volatility) have two important consequences: first, they also appear in suitably structured agent‐based models where, like real social actors, agents are socially embedded and metastable; second, their presence precludes the use of many standard statistical techniques like the chi‐square test. These characteristics in time‐series data indicate that a suitable agent‐based model rather than a standard statistical model will be appropriate. This is illustrated with an agent‐based model of mutual social influence on domestic water demand. The consequences for many frequently used statistical techniques are discussed."

--- How did I not know about this one before?
--- ETA after reading: [http://bactra.org/notebooks/agent-based-modeling.html#moss-edmonds]]]></description>
<dc:subject>in_NB agent-based_models social_science_methodology model_checking re:stacs have_read heavy_tails social_influence</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:c42d6e9e28bb/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:agent-based_models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_science_methodology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:model_checking"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:stacs"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:heavy_tails"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_influence"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.science.org/doi/10.1126/sciadv.abe5641">
    <title>How social learning amplifies moral outrage expression in online social networks</title>
    <dc:date>2021-12-17T16:34:58+00:00</dc:date>
    <link>https://www.science.org/doi/10.1126/sciadv.abe5641</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Moral outrage shapes fundamental aspects of social life and is now widespread in online social networks. Here, we show how social learning processes amplify online moral outrage expressions over time. In two preregistered observational studies on Twitter (7331 users and 12.7 million total tweets) and two preregistered behavioral experiments (N = 240), we find that positive social feedback for outrage expressions increases the likelihood of future outrage expressions, consistent with principles of reinforcement learning. In addition, users conform their outrage expressions to the expressive norms of their social networks, suggesting norm learning also guides online outrage expressions. Norm learning overshadows reinforcement learning when normative information is readily observable: in ideologically extreme networks, where outrage expression is more common, users are less sensitive to social feedback when deciding whether to express outrage. Our findings highlight how platform design interacts with human learning mechanisms to affect moral discourse in digital public spaces."]]></description>
<dc:subject>to:NB to_read social_media information_cascades social_influence re:actually-dr-internet-is-the-name-of-the-monsters-creator</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:c56dfcf7160d/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_media"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:information_cascades"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_influence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:actually-dr-internet-is-the-name-of-the-monsters-creator"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.cambridge.org/core/journals/british-journal-of-political-science/article/interaction-of-structural-factors-and-diffusion-in-social-unrest-evidence-from-the-swing-riots/A47B26A2C4B4A196207181DC7737627E">
    <title>The Interaction of Structural Factors and Diffusion in Social Unrest: Evidence from the Swing Riots | British Journal of Political Science | Cambridge Core</title>
    <dc:date>2021-12-05T17:02:42+00:00</dc:date>
    <link>https://www.cambridge.org/core/journals/british-journal-of-political-science/article/interaction-of-structural-factors-and-diffusion-in-social-unrest-evidence-from-the-swing-riots/A47B26A2C4B4A196207181DC7737627E</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Studies of the causes of social unrest typically focus on structural factors or diffusion. This article demonstrates the importance of considering their interaction and reveals a complex interplay between the two. This interaction is examined in the context of the English Swing riots of 1830–1831, in which it is possible to observe the structural factors relevant to each specific incident; this is often impossible when analyzing more recent cases of unrest. The authors find that the riots were triggered by economic factors and that diffusion more than tripled the direct effect of changes in local factors. Economic factors and the presence of potential riot leaders made an area more susceptible to the incoming diffusion of riots. The ways in which structural factors and diffusion interact is relevant to both historical and recent instances of social unrest."]]></description>
<dc:subject>to:NB diffusion_of_innovations social_influence re:homophily_and_confounding industrial_revolution color_me_skeptical via:?</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:4ad8e48c9ee0/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:diffusion_of_innovations"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_influence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:homophily_and_confounding"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:industrial_revolution"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:color_me_skeptical"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:?"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.journals.uchicago.edu/doi/10.1086/714784">
    <title>The Social Dynamics of Collective Action: Evidence from the Diffusion of the Swing Riots, 1830–1831 | The Journal of Politics</title>
    <dc:date>2021-12-05T17:02:09+00:00</dc:date>
    <link>https://www.journals.uchicago.edu/doi/10.1086/714784</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Social unrest often begins suddenly and spreads quickly. What is the information that drives its diffusion? How is this information transmitted? And who responds to this information? We present a general framework that emphasizes three aspects of the diffusion process: the networks through which information travels, whether information about repression affects participation, and the role of organizers. We use this framework to derive empirical hypotheses that we test in the context of the English Swing riots of 1830–31. This was the foundational case in the study of unrest in social history, and our identification strategy relies on spatiotemporal variation particular to this historical period. We find that diffusion was significant and that information about the riots traveled through personal and trade networks but not through transport or mass media networks. This information was not about repression, and local organizers played an important role in the diffusion of the riots."]]></description>
<dc:subject>to:NB diffusion_of_innovations social_influence re:homophily_and_confounding industrial_revolution color_me_skeptical via:?</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:48269ecc46e1/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:diffusion_of_innovations"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_influence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:homophily_and_confounding"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:industrial_revolution"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:color_me_skeptical"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:?"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2011.05774">
    <title>[2011.05774] Influencing dynamics on social networks without knowledge of network microstructure</title>
    <dc:date>2021-07-29T17:40:41+00:00</dc:date>
    <link>https://arxiv.org/abs/2011.05774</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Social network based information campaigns can be used for promoting beneficial health behaviours and mitigating polarisation (e.g. regarding climate change or vaccines). Network-based intervention strategies typically rely on full knowledge of network structure. It is largely not possible or desirable to obtain population-level social network data due to availability and privacy issues. It is easier to obtain information about individuals' attributes (e.g. age, income), which are jointly informative of an individual's opinions and their social network position. We investigate strategies for influencing the system state in a statistical mechanics based model of opinion formation. Using synthetic and data based examples we illustrate the advantages of implementing coarse-grained influence strategies on Ising models with modular structure in the presence of external fields. Our work provides a scalable methodology for influencing Ising systems on large graphs and the first exploration of the Ising influence problem in the presence of ambient (social) fields. By exploiting the observation that strong ambient fields can simplify control of networked dynamics, our findings open the possibility of efficiently computing and implementing public information campaigns using insights from social network theory without costly or invasive levels of data collection."]]></description>
<dc:subject>social_influence of_course_its_really_a_spin_glass in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:bcd2f414c4af/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_influence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:of_course_its_really_a_spin_glass"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://journals.sagepub.com/doi/abs/10.1177/00491241211014237">
    <title>Network Diffusion Under Homophily and Consolidation as a Mechanism for Social Inequality - Linda Zhao, Filiz Garip, 2021</title>
    <dc:date>2021-06-01T17:51:06+00:00</dc:date>
    <link>https://journals.sagepub.com/doi/abs/10.1177/00491241211014237</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Network externalities (where the value of a practice is a function of network alters that have already adopted the practice) are mechanisms that exacerbate social inequality under the condition of homophily (where advantaged individuals poised to be primary adopters are socially connected to other advantaged individuals). The authors use an agent-based model of diffusion on a real-life population for empirical illustration and, thus, do not consider consolidation (correlation between traits), a population parameter that shapes network structure and diffusion. Using an agent-based model, this article shows that prior findings linking homophily to segregated social ties and to differential diffusion outcomes are contingent on high levels of consolidation. Homophily, under low consolidation, is not sufficient to exacerbate existing differences in adoption probabilities across groups and can even end up alleviating intergroup inequality by facilitating diffusion."

--- Huh?  Isn't "consolidation" going to be automatic when multiple traits diffuse over the _same_ network?]]></description>
<dc:subject>to:NB to_read agent-based_models social_influence contagion homophily re:homophily_and_confounding inequality to_teach:statistics_of_inequality_and_discrimination color_me_skeptical</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:511133332421/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:agent-based_models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_influence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:contagion"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:homophily"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:homophily_and_confounding"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:inequality"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:statistics_of_inequality_and_discrimination"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:color_me_skeptical"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2105.05662">
    <title>[2105.05662] Transfer entropy dependent on distance among agents in quantifying leader-follower relationships</title>
    <dc:date>2021-05-13T14:22:13+00:00</dc:date>
    <link>https://arxiv.org/abs/2105.05662</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Synchronized movement of (both unicellular and multicellular) systems can be observed almost everywhere. Understanding of how organisms are regulated to synchronized behavior is one of the challenging issues in the field of collective motion. It is hypothesized that one or a few agents in a group regulate(s) the dynamics of the whole collective, known as leader(s). The identification of the leader (influential) agent(s) is very crucial. This article reviews different mathematical models that represent different types of leadership. We focus on the improvement of the leader-follower classification problem. It was found using a simulation model that the use of interaction domain information significantly improves the leader-follower classification ability using both linear schemes and information-theoretic schemes for quantifying influence. This article also reviews different schemes that can be used to identify the interaction domain using the motion data of agents."

--- Last tag because, from what I admit is a _very_ superficial skim, they seem to be _presuming_ leader-follower relationships, rather than (say) mutual influence (as in classic flocking models), common environmental influences, homophily, etc.]]></description>
<dc:subject>to:NB social_influence information_theory causal_inference re:homophily_and_confounding color_me_skeptical</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:ebf602026839/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_influence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:information_theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:causal_inference"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:homophily_and_confounding"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:color_me_skeptical"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://doi.org/10.1093/sf/sou101">
    <title>Inequality Preservation through Uneven Diffusion of Cultural Materials across Stratified Groups</title>
    <dc:date>2021-05-06T17:54:13+00:00</dc:date>
    <link>https://doi.org/10.1093/sf/sou101</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Inequality between groups is frequently maintained through the construction and
legitimation of inter-group cultural differences. I draw on Blau’s multiform heterogeneity and complex contagion models to theorize and develop a relational mechanism that shows how inequality can be preserved when additional, new bases of
differentiating between groups layer over existing ones. I investigate the conditions
under which variations in the distribution of the population across stratified groups
and homophily of social networks along the stratifying attribute interact in such a way
that a belief/practice diffuses widely in one group but not the other—an outcome
referred to as differential diffusion. I also analyze how size of ego networks and adoption thresholds affect differential diffusion. Using mathematical and agent-based models, I find a positive correlation between adoption thresholds and homophily: when
social networks are highly homophilous (e.g., race and socioeconomic class), uneven
diffusion of non-normative behavior reproduces inequality; inclusive networks (e.g., in
diverse city schools), in contrast, reestablish inequality through differential diffusion of
low-risk behavior. This suggests that cultivating diversity is likely to mitigate inequality
preservation in conservative situations where adoption of new beliefs/practices needs
considerable affirmation. Encouraging status-based solidarity is more appropriate in
receptive contexts where adoption of new behaviors entails comparatively lower risk.
The results also imply that analyses of diffusion need to be sensitive to contextual
factors, including homophily, cultural institutionalization of the diffusing material, and
population distribution. Finally, I extend Ridgeway’s seminal work to show how relational structure can not only construct status hierarchies but also contribute to their
symbolic maintenance."]]></description>
<dc:subject>to:NB sociology diffusion_of_innovations social_networks social_influence inequality homophily re:do-institutions-evolve to_teach:statistics_of_inequality_and_discrimination</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:154adfff20be/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:sociology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:diffusion_of_innovations"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_influence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:inequality"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:homophily"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:do-institutions-evolve"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:statistics_of_inequality_and_discrimination"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2104.10365">
    <title>[2104.10365] Identification of Peer Effects with Miss-specified Peer Groups: Missing Data and Group Uncertainty</title>
    <dc:date>2021-04-22T15:17:18+00:00</dc:date>
    <link>https://arxiv.org/abs/2104.10365</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We consider identification of peer effects under peer group miss-specification. Our model of group miss-specification allows for missing data and peer group uncertainty. Missing data can take the form of some individuals being completely absent from the data, and the researcher need not have any information on these individuals and may not even know that they are missing. We show that peer effects are nevertheless identifiable if these individuals are missing completely at random or missing at random, and propose a GMM estimator which jointly estimates the sampling probability and peer effects. In practice this means that the researcher need only have access to an individual/household level sample with group identifiers. The researcher may also be uncertain as to what is the relevant peer group for the outcome under study. We show that peer effects are nevertheless identifiable provided that the candidate peer groups are nested within one another (e.g. classroom, grade, school) and propose a non-linear least squares estimator. We conduct a Monte-Carlo experiment to demonstrate our identification results and the performance of the proposed estimators in a setting tailored to real data (the Dartmouth room-mate data)."]]></description>
<dc:subject>to:NB social_influence re:homophily_and_confounding color_me_skeptical</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:304dc1bb943e/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_influence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:homophily_and_confounding"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:color_me_skeptical"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2006.09938">
    <title>[2006.09938] Did State-sponsored Trolls Shape the 2016 US Presidential Election Discourse? Quantifying Influence on Twitter</title>
    <dc:date>2021-04-16T16:01:00+00:00</dc:date>
    <link>https://arxiv.org/abs/2006.09938</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["It is a widely accepted fact that state-sponsored Twitter accounts operated during the 2016 US presidential election, spreading millions of tweets with misinformation and inflammatory political content. Whether these social media campaigns of the so-called "troll" accounts were able to manipulate public opinion is still in question. Here, we quantify the influence of troll accounts on Twitter by analyzing 152.5 million tweets (by 9.9 million users) from that period. The data contain original tweets from 822 troll accounts identified as such by Twitter itself. We construct and analyse a very large interaction graph of 9.3 million nodes and 169.9 million edges using graph analysis techniques, along with a game-theoretic centrality measure. Then, we quantify the influence of all Twitter accounts on the overall information exchange as is defined by the retweet cascades. We provide a global influence ranking of all Twitter accounts and we find that one troll account appears in the top-100 and four in the top-1000. This combined with other findings presented in this paper constitute evidence that the driving force of virality and influence in the network came from regular users - users who have not been classified as trolls by Twitter. On the other hand, we find that on average, troll accounts were tens of times more influential than regular users were. Moreover, 23% and 22% of regular accounts in the top-100 and top-1000 respectively, have now been suspended by Twitter. This raises questions about their authenticity and practices during the 2016 US presidential election."

--- This doesn't seem to say anything about voting.]]></description>
<dc:subject>to:NB us_politics deceiving_us_has_become_an_industrial_process social_influence social_media information_cascades re:actually-dr-internet-is-the-name-of-the-monsters-creator twitter</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:8ec8009b0053/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:us_politics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:deceiving_us_has_become_an_industrial_process"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_influence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_media"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:information_cascades"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:actually-dr-internet-is-the-name-of-the-monsters-creator"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:twitter"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1812.02276">
    <title>[1812.02276] Identifying the Effect of Persuasion</title>
    <dc:date>2021-04-12T03:08:24+00:00</dc:date>
    <link>https://arxiv.org/abs/1812.02276</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We set up an econometric model of persuasion and study identification of key parameters under various scenarios of data availability. We find that a commonly used measure of persuasion does not estimate the persuasion rate of any population in general. We provide formal identification results, recommend several new parameters to estimate, and discuss their interpretation. Further, we propose methods for carrying out inference. We revisit the empirical literature on persuasion to show that the persuasive effect is highly heterogeneous. We also show that the existence of a continuous instrument opens up the possibility of point identification for the policy-relevant population."]]></description>
<dc:subject>to:NB influence econometrics social_measurement social_influence causal_inference</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:bcf0284f77c0/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:influence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:econometrics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_measurement"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_influence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:causal_inference"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.pnas.org/content/118/7/e2013391118">
    <title>Network hubs cease to be influential in the presence of low levels of advertising | PNAS</title>
    <dc:date>2021-02-13T03:12:50+00:00</dc:date>
    <link>https://www.pnas.org/content/118/7/e2013391118</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Attempts to find central “influencers,” “opinion leaders,” “hubs,” “optimal seeds,” or other important people who can hasten or slow diffusion or social contagion has long been a major research question in network science. We demonstrate that opinion leadership occurs only under conventional but implausible scope conditions. We demonstrate that a highly central node is a more effective seed for diffusion than a random node if nodes can only learn via the network. However, actors are also subject to external influences such as mass media and advertising. We find that diffusion is noticeably faster when it begins with a high centrality node, but that this advantage only occurs in the region of parameter space where external influence is constrained to zero and collapses catastrophically even at minimal levels of external influence. Importantly, nearly all prior agent-based research on choosing a seed or seeds implicitly occurs in the network influence only region of parameter space. We demonstrate this effect using preferential attachment, small world, and several empirical networks. These networks vary in how large the baseline opinion leadership effect is, but in all of them it collapses with the introduction of external influence. This implies that, in marketing and public health, advertising broadly may be underrated as a strategy for promoting network-based diffusion."

--- This is a lovely little paper, which makes an important point convincingly and clearly.  I'm torn between admiration, kicking myself for not having thought about this, and wanting to teach it.
--- On p. 2, right column, for "$\beta=0$", read "$\alpha=0$".
--- See if we can invite GR to Networkshop?
]]></description>
<dc:subject>contagion social_networks social_influence sociology advertising rossman.gabriel re:do-institutions-evolve have_read to_teach:baby-nets to_teach:complexity-and-inference in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:1bc4e8c93723/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:contagion"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_influence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:sociology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:advertising"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:rossman.gabriel"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:do-institutions-evolve"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:baby-nets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:complexity-and-inference"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.pnas.org/content/118/6/e2019375118">
    <title>Social penumbras predict political attitudes | PNAS</title>
    <dc:date>2021-02-06T20:01:49+00:00</dc:date>
    <link>https://www.pnas.org/content/118/6/e2019375118</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["To explain the political clout of different social groups, traditional accounts typically focus on the group’s size, resources, or commonality and intensity of its members’ interests. We contend that a group’s penumbra—the set of individuals who are personally familiar with people in that group—is another important explanatory factor that merits systematic analysis. To this end, we designed a panel study that allows us to learn about the characteristics of the penumbras of politically relevant groups such as gay people, the unemployed, or recent immigrants. Our study reveals major and systematic differences in the penumbras of various social groups, even ones of similar size. Moreover, we find evidence that entering a group’s penumbra is associated with a change in attitude on group-related policy questions. Taken together, our findings suggest that penumbras are pertinent for understanding variation in the political standing of different groups in society."]]></description>
<dc:subject>to:NB social_networks social_influence political_science gelman.andrew to_read re:homophily_and_confounding</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:ffe54b6dbeef/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_influence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:political_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:gelman.andrew"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:homophily_and_confounding"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.cambridge.org/core/journals/network-science/article/abs/sensitivity-analysis-for-network-observations-with-applications-to-inferences-of-social-influence-effects/C93F31E38EA80360A8A9725AEB1EBDEE">
    <title>Sensitivity analysis for network observations with applications to inferences of social influence effects | Network Science | Cambridge Core</title>
    <dc:date>2021-02-05T22:24:59+00:00</dc:date>
    <link>https://www.cambridge.org/core/journals/network-science/article/abs/sensitivity-analysis-for-network-observations-with-applications-to-inferences-of-social-influence-effects/C93F31E38EA80360A8A9725AEB1EBDEE</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["The validity of network observations is sometimes of concern in empirical studies, since observed networks are prone to error and may not represent the population of interest. This lack of validity is not just a result of random measurement error, but often due to systematic bias that can lead to the misinterpretation of actors’ preferences of network selections. These issues in network observations could bias the estimation of common network models (such as those pertaining to influence and selection) and lead to erroneous statistical inferences. In this study, we proposed a simulation-based sensitivity analysis method that can evaluate the robustness of inferences made in social network analysis to six forms of selection mechanisms that can cause biases in network observations—random, homophily, anti-homophily, transitivity, reciprocity, and preferential attachment. We then applied this sensitivity analysis to test the robustness of inferences for social influence effects, and we derived two sets of analytical solutions that can account for biases in network observations due to random, homophily, and anti-homophily selection."]]></description>
<dc:subject>to:NB network_data_analysis social_influence model_checking re:homophily_and_confounding</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:ee33c5418a42/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:network_data_analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_influence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:model_checking"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:homophily_and_confounding"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2102.02382">
    <title>[2102.02382] Mainstreaming of conspiracy theories and misinformation</title>
    <dc:date>2021-02-05T20:23:04+00:00</dc:date>
    <link>https://arxiv.org/abs/2102.02382</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Parents - particularly moms - increasingly consult social media for support when taking decisions about their young children, and likely also when advising other family members such as elderly relatives. Minimizing malignant online influences is therefore crucial to securing their assent for policies ranging from vaccinations, masks and social distancing against the pandemic, to household best practices against climate change, to acceptance of future 5G towers nearby. Here we show how a strengthening of bonds across online communities during the pandemic, has led to non-Covid-19 conspiracy theories (e.g. fluoride, chemtrails, 5G) attaining heightened access to mainstream parent communities. Alternative health communities act as the critical conduits between conspiracy theorists and parents, and make the narratives more palatable to the latter. We demonstrate experimentally that these inter-community bonds can perpetually generate new misinformation, irrespective of any changes in factual information. Our findings show explicitly why Facebook's current policies have failed to stop the mainstreaming of non-Covid-19 and Covid-19 conspiracy theories and misinformation, and why targeting the largest communities will not work. A simple yet exactly solvable and empirically grounded mathematical model, shows how modest tailoring of mainstream communities' couplings could prevent them from tipping against establishment guidance. Our conclusions should also apply to other social media platforms and topics."]]></description>
<dc:subject>epidemiology_of_representations social_networks social_influence social_life_of_the_mind psychoceramics re:actually-dr-internet-is-the-name-of-the-monsters-creator networked_life in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:ff7320ecec8b/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:epidemiology_of_representations"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_influence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_life_of_the_mind"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:psychoceramics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:actually-dr-internet-is-the-name-of-the-monsters-creator"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:networked_life"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://journals.aps.org/pre/abstract/10.1103/PhysRevE.103.022303">
    <title>Phys. Rev. E 103, 022303 (2021) - Social contagion in a world with asymmetric influence</title>
    <dc:date>2021-02-05T19:37:01+00:00</dc:date>
    <link>https://journals.aps.org/pre/abstract/10.1103/PhysRevE.103.022303</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Social media has blurred the distinction between news outlets and social networks by giving everyone access to mass communication. We simulate how influencers compete for attention on a social network by spreading information. The network structure occupies an ordered metastable state where one influencer maintains dominance for a sustained period or a fragmented state that divides attention between influencers. Numerical simulations are performed to map the domain of the ordered regime on various network topologies. Mutual coexistence between a few dominating influencers occurs on a scale-free social network. Our findings suggest the perception of fake news as a pervasive problem is endemic to a society where everyone can become a news outlet."]]></description>
<dc:subject>to:NB social_influence contagion of_course_its_really_a_spin_glass re:actually-dr-internet-is-the-name-of-the-monsters-creator color_me_skeptical metastability</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:f12b301b7c6d/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_influence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:contagion"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:of_course_its_really_a_spin_glass"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:actually-dr-internet-is-the-name-of-the-monsters-creator"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:color_me_skeptical"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:metastability"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2007.00601">
    <title>[2007.00601] Emergence of polarized ideological opinions in multidimensional topic spaces</title>
    <dc:date>2021-02-04T15:23:03+00:00</dc:date>
    <link>https://arxiv.org/abs/2007.00601</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Opinion polarization is on the rise, causing concerns for the openness of public debates. Additionally, extreme opinions on different topics often show significant correlations. The dynamics leading to these polarized ideological opinions pose a challenge: How can such correlations emerge, without assuming them a priori in the individual preferences or in a preexisting social structure? Here we propose a simple model that qualitatively reproduces ideological opinion states found in survey data, even between rather unrelated, but sufficiently controversial, topics. Inspired by skew coordinate systems recently proposed in natural language processing models, we solidify these intuitions in a formalism of opinions unfolding in a multidimensional space where topics form a non-orthogonal basis. Opinions evolve according to the social interactions among the agents, which are ruled by homophily: two agents sharing similar opinions are more likely to interact. The model features phase transitions between a global consensus, opinion polarization, and ideological states. Interestingly, the ideological phase emerges by relaxing the assumption of an orthogonal basis of the topic space, i.e. if topics thematically overlap. Furthermore, we analytically and numerically show that these transitions are driven by the controversialness of the topics discussed, the more controversial the topics, the more likely are opinion to be correlated. Our findings shed light upon the mechanisms driving the emergence of ideology in the formation of opinions."

--- To compare carefully with the following model: Everyone has an IID random score on a large (p) number of very small scale issues/preferences.  Actual public disputes reflect a small number (q) of questions, and each question randomly samples a subset, typically k, of the fine-grained issues.  People actually say "yes" or "no" to each question based on whether their summed score is positive or negative.  Experience with the Thomson ability-sampling model [http://bactra.org/notebooks/thomson-sampling-model.html] suggests that this will generate the appearance of a one-dimensional axis for opinions.  But I do need to actually do the algebra there.]]></description>
<dc:subject>to:NB to_read polarization of_course_its_really_a_spin_glass social_influence</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:e6d980ec8775/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:polarization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:of_course_its_really_a_spin_glass"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_influence"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.aeaweb.org/articles?id=10.1257/aer.20171611">
    <title>Speculative Fever: Investor Contagion in the Housing Bubble - American Economic Association</title>
    <dc:date>2021-01-28T17:21:06+00:00</dc:date>
    <link>https://www.aeaweb.org/articles?id=10.1257/aer.20171611</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Historical anecdotes abound of new investors being drawn into a booming asset market, only to suffer when the market turns. While the role of investor contagion in asset bubbles has been explored extensively in the theoretical literature, causal empirical evidence on the topic is much rarer. This paper studies the recent boom and bust in the US housing market and establishes that many novice investors entered the market as a direct result of observing investing activity of multiple forms in their own neighborhoods and that "infected" investors performed poorly relative to other investors along several dimensions."]]></description>
<dc:subject>to:NB economics economic_history finance financial_crisis_of_2007-- information_cascades social_influence contagion re:homophily_and_confounding to_read not_at_all_related_to_current_events</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:c60c9c843f6c/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:economics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:economic_history"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:finance"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:financial_crisis_of_2007--"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:information_cascades"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_influence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:contagion"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:homophily_and_confounding"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:not_at_all_related_to_current_events"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://science.sciencemag.org/content/341/6146/647">
    <title>Social Influence Bias: A Randomized Experiment | Science</title>
    <dc:date>2021-01-13T01:04:08+00:00</dc:date>
    <link>https://science.sciencemag.org/content/341/6146/647</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Our society is increasingly relying on the digitized, aggregated opinions of others to make decisions. We therefore designed and analyzed a large-scale randomized experiment on a social news aggregation Web site to investigate whether knowledge of such aggregates distorts decision-making. Prior ratings created significant bias in individual rating behavior, and positive and negative social influences created asymmetric herding effects. Whereas negative social influence inspired users to correct manipulated ratings, positive social influence increased the likelihood of positive ratings by 32% and created accumulating positive herding that increased final ratings by 25% on average. This positive herding was topic-dependent and affected by whether individuals were viewing the opinions of friends or enemies. A mixture of changing opinion and greater turnout under both manipulations together with a natural tendency to up-vote on the site combined to create the herding effects. Such findings will help interpret collective judgment accurately and avoid social influence bias in collective intelligence in the future."]]></description>
<dc:subject>to:NB social_influence experimental_sociology networked_life taylor.sean aral.sinan to_read</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:a939a1e95e03/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_influence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:experimental_sociology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:networked_life"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:taylor.sean"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:aral.sinan"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2005.10879">
    <title>[2005.10879] Automatic Detection of Influential Actors in Disinformation Networks</title>
    <dc:date>2021-01-11T15:25:15+00:00</dc:date>
    <link>https://arxiv.org/abs/2005.10879</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["The weaponization of digital communications and social media to conduct disinformation campaigns at immense scale, speed, and reach presents new challenges to identify and counter hostile influence operations (IOs). This paper presents an end-to-end framework to automate detection of disinformation narratives, networks, and influential actors. The framework integrates natural language processing, machine learning, graph analytics, and a novel network causal inference approach to quantify the impact of individual actors in spreading IO narratives. We demonstrate its capability on real-world hostile IO campaigns with Twitter datasets collected during the 2017 French presidential elections, and known IO accounts disclosed by Twitter over a broad range of IO campaigns (May 2007 to February 2020), over 50,000 accounts, 17 countries, and different account types including both trolls and bots. Our system detects IO accounts with 96% precision, 79% recall, and 96% area-under-the-PR-curve, maps out salient network communities, and discovers high-impact accounts that escape the lens of traditional impact statistics based on activity counts and network centrality. Results are corroborated with independent sources of known IO accounts from U.S. Congressional reports, investigative journalism, and IO datasets provided by Twitter."

--- Last tag because I don't see how they could possibly solve the confounding problem, except by wishing it away ("identification assumptions").]]></description>
<dc:subject>to:NB causal_inference social_influence epidemiology_of_representations deceiving_us_has_become_an_industrial_process networked_life re:homophily_and_confounding rubin.donald_b. color_me_skeptical</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:ee91b1cefc6f/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:causal_inference"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_influence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:epidemiology_of_representations"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:deceiving_us_has_become_an_industrial_process"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:networked_life"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:homophily_and_confounding"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:rubin.donald_b."/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:color_me_skeptical"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://science.sciencemag.org/content/371/6525/153">
    <title>Anterior cingulate inputs to nucleus accumbens control the social transfer of pain and analgesia | Science</title>
    <dc:date>2021-01-08T03:37:01+00:00</dc:date>
    <link>https://science.sciencemag.org/content/371/6525/153</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Empathy is an essential component of social communication that involves experiencing others’ sensory and emotional states. We observed that a brief social interaction with a mouse experiencing pain or morphine analgesia resulted in the transfer of these experiences to its social partner. Optogenetic manipulations demonstrated that the anterior cingulate cortex (ACC) and its projections to the nucleus accumbens (NAc) were selectively involved in the social transfer of both pain and analgesia. By contrast, the ACC→NAc circuit was not necessary for the social transfer of fear, which instead depended on ACC projections to the basolateral amygdala. These findings reveal that the ACC, a brain area strongly implicated in human empathic responses, mediates distinct forms of empathy in mice by influencing different downstream targets."]]></description>
<dc:subject>to:NB neuroscience social_influence contagion empathy</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:a9ec49c3cfcd/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:neuroscience"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_influence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:contagion"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:empathy"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arcdigital.media/no-professors-are-not-brainwashing-their-students-d4694522f413">
    <title>No, Professors Are Not Brainwashing Their Students | by Jeffrey Sachs | Arc Digital</title>
    <dc:date>2020-12-26T18:15:45+00:00</dc:date>
    <link>https://arcdigital.media/no-professors-are-not-brainwashing-their-students-d4694522f413</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>academia us_culture_wars social_influence</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:d0cf3338b459/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:academia"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:us_culture_wars"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_influence"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2012.12309">
    <title>[2012.12309] Influence Maximization Under Generic Threshold-based Non-submodular Model</title>
    <dc:date>2020-12-26T17:40:14+00:00</dc:date>
    <link>https://arxiv.org/abs/2012.12309</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["As a widely observable social effect, influence diffusion refers to a process where innovations, trends, awareness, etc. spread across the network via the social impact among individuals. Motivated by such social effect, the concept of influence maximization is coined, where the goal is to select a bounded number of the most influential nodes (seed nodes) from a social network so that they can jointly trigger the maximal influence diffusion. A rich body of research in this area is performed under statistical diffusion models with provable submodularity, which essentially simplifies the problem as the optimal result can be approximated by the simple greedy search. When the diffusion models are non-submodular, however, the research community mostly focuses on how to bound/approximate them by tractable submodular functions so as to estimate the optimal result. In other words, there is still a lack of efficient methods that can directly resolve non-submodular influence maximization problems. In this regard, we fill the gap by proposing seed selection strategies using network graphical properties in a generalized threshold-based model, called influence barricade model, which is non-submodular. Specifically, under this model, we first establish theories to reveal graphical conditions that ensure the network generated by node removals has the same optimal seed set as that in the original network. We then exploit these theoretical conditions to develop efficient algorithms by strategically removing less-important nodes and selecting seeds only in the remaining network. To the best of our knowledge, this is the first graph-based approach that directly tackles non-submodular influence maximization."

--- Of course, social influence is not observationally identified, and from what I can tell this whole literature just ignores issues of homophily (even homophily on measured covariates...), but this looks interesting within that mathematical game.]]></description>
<dc:subject>to:NB social_networks social_influence optimization graph_theory re:do-institutions-evolve</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:aafc35a577e2/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_influence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:optimization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:graph_theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:do-institutions-evolve"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://osf.io/preprints/socarxiv/ec46t/">
    <title>SocArXiv Papers | A model-based method for detecting persistent cultural change using panel data</title>
    <dc:date>2020-12-23T03:34:54+00:00</dc:date>
    <link>https://osf.io/preprints/socarxiv/ec46t/</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Kiley and Vaisey (2020) recently published a method for assessing whether survey respondents appear to be changing their beliefs between waves or whether they instead appear to be repeating fixed responses with temporary local influences. This question is important because these processes reflect very different theoretical models of the evolution of “personal culture.” That is, if cultural beliefs are primarily public and responsive to external discourse, we should observe more updating as people respond to changes in their local environment. On the other hand, if cultural beliefs are primarily something learned early, then “settled dispositions” should be relatively resilient to change. In this paper, we build on Kiley and Vaisey (2020) and introduce an alternative method for distinguishing between cases where respondents appear be actively updating their responses and situations where respondents’ responses appear to be settled. This method, based on structural equation modeling, provides a close fit to the theoretical models outlined in Kiley and Vaisey (2020) and provides even stronger support for their claim that most survey responses reflect settled dispositions developed prior to adulthood."]]></description>
<dc:subject>to:NB social_science_methodology sociology time_series surveys social_influence re:homophily_and_confounding</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:6f0deaa8da89/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_science_methodology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:sociology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:time_series"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:surveys"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_influence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:homophily_and_confounding"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2012.08925">
    <title>[2012.08925] Analysing the Social Spread of Behaviour: Integrating Complex Contagions into Network Based Diffusions</title>
    <dc:date>2020-12-17T15:20:31+00:00</dc:date>
    <link>https://arxiv.org/abs/2012.08925</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["The spread of socially-learnt behaviours occurs in many animal species, and understanding how behaviours spread can provide novel insights into the causes and consequences of sociality. Within wild populations, behaviour spread is often assumed to occur as a "simple contagion". Yet, emerging evidence suggests behaviours may frequently spread as "complex contagions", and this holds significant ramifications for the modes and extent of transmission. We present a new framework enabling comprehensive examination of behavioural contagions by integrating social-learning strategies into network-based diffusion analyses. We show how our approach allows determination of the relationship between social bonds and behavioural transmission, identification of individual-level transmission rules, and examination of population-level social structure effects. We provide resources that allow general applications across diverse systems, and demonstrate how further study-specific developments can be made. Finally, we outline the new opportunities this framework facilitates, the conceptual contributions to understanding sociality, and its applications across fields."

--- The word "homophily" does not appear in this paper...]]></description>
<dc:subject>to:NB social_networks social_influence diffusion_of_innovations social_life_of_the_mind re:homophily_and_confounding re:do-institutions-evolve to_read</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:4c2752227906/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_influence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:diffusion_of_innovations"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_life_of_the_mind"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:homophily_and_confounding"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:do-institutions-evolve"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2012.08572">
    <title>[2012.08572] An Agenda for Disinformation Research</title>
    <dc:date>2020-12-17T15:19:20+00:00</dc:date>
    <link>https://arxiv.org/abs/2012.08572</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["In the 21st Century information environment, adversarial actors use disinformation to manipulate public opinion. The distribution of false, misleading, or inaccurate information with the intent to deceive is an existential threat to the United States--distortion of information erodes trust in the socio-political institutions that are the fundamental fabric of democracy: legitimate news sources, scientists, experts, and even fellow citizens. As a result, it becomes difficult for society to come together within a shared reality; the common ground needed to function effectively as an economy and a nation. Computing and communication technologies have facilitated the exchange of information at unprecedented speeds and scales. This has had countless benefits to society and the economy, but it has also played a fundamental role in the rising volume, variety, and velocity of disinformation. Technological advances have created new opportunities for manipulation, influence, and deceit. They have effectively lowered the barriers to reaching large audiences, diminishing the role of traditional mass media along with the editorial oversight they provided. The digitization of information exchange, however, also makes the practices of disinformation detectable, the networks of influence discernable, and suspicious content characterizable. New tools and approaches must be developed to leverage these affordances to understand and address this growing challenge."]]></description>
<dc:subject>to:NB epidemiology_of_representations networked_life propaganda kith_and_kin wiggins.chris starbird.kate menczer.filippo bradley.elizabeth network_data_analysis re:homophily_and_confounding social_influence</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:f1530164373c/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:epidemiology_of_representations"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:networked_life"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:propaganda"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:kith_and_kin"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:wiggins.chris"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:starbird.kate"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:menczer.filippo"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:bradley.elizabeth"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:network_data_analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:homophily_and_confounding"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_influence"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://journals.sagepub.com/doi/abs/10.1177/0049124119852369">
    <title>A Comparison of Peer Influence Estimates from SIENA Stochastic Actor–based Models and from Conventional Regression Approaches - Daniel T. Ragan, D. Wayne Osgood, Nayan G. Ramirez, James Moody, Scott D. Gest, 2019</title>
    <dc:date>2020-12-16T20:25:00+00:00</dc:date>
    <link>https://journals.sagepub.com/doi/abs/10.1177/0049124119852369</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["The current study compares estimates of peer influence from an analytic approach that explicitly address network processes with those from traditional approaches that do not. Using longitudinal network data from the PROmoting School–community–university Partnerships to Enhance Resilience peers project, we obtain estimates of social influence on multiple outcomes from both conventional linear modeling approaches and the stochastic actor–based modeling approach of the simulation investigation for empirical network analysis (SIENA) software. Our findings indicate that peer influence estimates from SIENA are not more conservative relative to other methods, that each method is subject to omitted variable bias from stable individual differences, and that imprecision among each method could lead to erroneous conclusions in samples that lack sufficient power. Together, these results underscore the difficulty in obtaining estimates of peer influence from observational data, and they give no indication that results from conventional methods tend to be biased toward overestimating peer influence, relative to SIENA."]]></description>
<dc:subject>re:homophily_and_confounding social_influence agent-based_models in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:d7809c4d11b2/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:homophily_and_confounding"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_influence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:agent-based_models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://sociologicalscience.com/articles-v7-18-433/">
    <title>The Meeting of Minds: Forging Social and Intellectual Networks within Universities | Sociological Science</title>
    <dc:date>2020-12-16T14:50:32+00:00</dc:date>
    <link>https://sociologicalscience.com/articles-v7-18-433/</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["How are social and intellectual relations structured and given shape within research universities? To answer these questions, we test to what extent various theoretically predicted processes explain the dynamics of academics’ networks of collaboration and shared language use in a unique longitudinal data set (1994 to 2005) of 2,631 faculty at a large private American university. Using the latest advances in stochastic actor-oriented models (in RSiena) and text analysis, we found that social and intellectual relations are clustered and centralized on bridging faculty who form a broader interdisciplinary hub of research in the university, and that, over time, this hub disseminated its style of (interdisciplinary) research to other faculty. These networks are shaped by selection based on age, gender, race, and academic rank as well as the coevolution of social and intellectual relations over time. Clear differences emerge in science, technology, engineering, and mathematics (STEM) fields and are strongly driven by structural mechanisms of clustering and centralization, whereas non-STEM fields (social sciences and humanities) are strongly driven by personal preferences of faculty members."]]></description>
<dc:subject>to:NB academia social_networks social_life_of_the_mind networks_in_and_over_time sociology social_influence re:homophily_and_confounding to_teach:baby-nets</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:bd443a6a31b9/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:academia"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_life_of_the_mind"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:networks_in_and_over_time"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:sociology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_influence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:homophily_and_confounding"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:baby-nets"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.kayladelahaye.net/outcomes2">
    <title>Outcomes — Kayla de la Haye</title>
    <dc:date>2020-12-14T15:11:53+00:00</dc:date>
    <link>https://www.kayladelahaye.net/outcomes2</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>social_networks social_influence obesity re:homophily_and_confounding</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:f7ecfdb34fda/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_influence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:obesity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:homophily_and_confounding"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2011.15083">
    <title>[2011.15083] A Large Scale Randomized Controlled Trial on Herding in Peer-Review Discussions</title>
    <dc:date>2020-12-02T01:41:31+00:00</dc:date>
    <link>https://arxiv.org/abs/2011.15083</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Peer review is the backbone of academia and humans constitute a cornerstone of this process, being responsible for reviewing papers and making the final acceptance/rejection decisions. Given that human decision making is known to be susceptible to various cognitive biases, it is important to understand which (if any) biases are present in the peer-review process and design the pipeline such that the impact of these biases is minimized. In this work, we focus on the dynamics of between-reviewers discussions and investigate the presence of herding behaviour therein. In that, we aim to understand whether reviewers and more senior decision makers get disproportionately influenced by the first argument presented in the discussion when (in case of reviewers) they form an independent opinion about the paper before discussing it with others. Specifically, in conjunction with the review process of ICML 2020 -- a large, top tier machine learning conference -- we design and execute a randomized controlled trial with the goal of testing for the conditional causal effect of the discussion initiator's opinion on the outcome of a paper."]]></description>
<dc:subject>to:NB peer_review social_influence science_as_a_social_process experimental_sociology</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:f5f08f703bdf/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:peer_review"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_influence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:science_as_a_social_process"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:experimental_sociology"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5296697/#__ffn_sectitle">
    <title>Mass Shootings: The Role of the Media in Promoting Generalized Imitation</title>
    <dc:date>2020-11-29T18:53:46+00:00</dc:date>
    <link>https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5296697/#__ffn_sectitle</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Mass shootings are a particular problem in the United States, with one mass shooting occurring approximately every 12.5 days.
"Recently a “contagion” effect has been suggested wherein the occurrence of one mass shooting increases the likelihood of another mass shooting occurring in the near future. Although contagion is a convenient metaphor used to describe the temporal spread of a behavior, it does not explain how the behavior spreads. Generalized imitation is proposed as a better model to explain how one person’s behavior can influence another person to engage in similar behavior.
"Here we provide an overview of generalized imitation and discuss how the way in which the media report a mass shooting can increase the likelihood of another shooting event. Also, we propose media reporting guidelines to minimize imitation and further decrease the likelihood of a mass shooting."]]></description>
<dc:subject>to:NB to_read contagion social_influence mass_shootings re:homophily_and_confounding re:statistics_of_muckers violence</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:e156d5a494ef/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:contagion"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_influence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:mass_shootings"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:homophily_and_confounding"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:statistics_of_muckers"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:violence"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0233458">
    <title>Neither influence nor selection: Examining co-evolution of political orientation and social networks in the NetSense and NetHealth studies</title>
    <dc:date>2020-07-13T18:02:26+00:00</dc:date>
    <link>https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0233458</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Political orientation is one of the most important and consequential individual attributes studied by social scientists. Yet, we know relatively little about the temporal evolution of political orientation, especially at periods in the life course during which individuals are forming new social relationships and transitioning to new relational contexts. Here we use Stochastic Actor-Oriented models (SAOMs) to examine the co-evolution of political orientation and social networks using two feature-rich, temporal network datasets from samples of students making the transition to college at the University of Notre Dame (i.e. the NetSense and NetHealth studies). Overall, we find a great deal of stability in political orientation, with a slight tendency for the 2011 NetSense study participants to become more conservative during their first four semesters in college, but not the 2015 NetHealth study participants. Partisanship is the best predictor of changes in political orientation, with students who identify or vote as Republicans becoming more conservative over time. Neither network influence nor selection processes seem to be driving observed changes. During this formative period, relatively stable identities such as party affiliation predict changes in political orientation independently of local network dynamics, selection processes, socio-demographic traits, and dispositional factors."]]></description>
<dc:subject>to:NB to_read social_influence social_networks sociology an_interesting_finding_of_no_effect lizardo.omar to_teach:baby-nets partisandship_and_polarization</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:bc00b5aec2a4/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_influence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:sociology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:an_interesting_finding_of_no_effect"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:lizardo.omar"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:baby-nets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:partisandship_and_polarization"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.journals.uchicago.edu/doi/abs/10.1086/599250?journalCode=ajs&amp;">
    <title>The False Enforcement of Unpopular Norms1 | American Journal of Sociology: Vol 115, No 2</title>
    <dc:date>2020-07-13T16:31:18+00:00</dc:date>
    <link>https://www.journals.uchicago.edu/doi/abs/10.1086/599250?journalCode=ajs&amp;</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Prevailing theory assumes that people enforce norms in order to pressure others to act in ways that they approve. Yet there are numerous examples of “unpopular norms” in which people compel each other to do things that they privately disapprove. While peer sanctioning suggests a ready explanation for why people conform to unpopular norms, it is harder to understand why they would enforce a norm they privately oppose. The authors argue that people enforce unpopular norms to show that they have complied out of genuine conviction and not because of social pressure. They use laboratory experiments to demonstrate this “false enforcement” in the context of a wine tasting and an academic text evaluation. Both studies find that participants who conformed to a norm due to social pressure then falsely enforced the norm by publicly criticizing a lone deviant. A third study shows that enforcement of a norm effectively signals the enforcer’s genuine support for the norm. These results demonstrate the potential for a vicious cycle in which perceived pressures to conform to and falsely enforce an unpopular norm reinforce one another."]]></description>
<dc:subject>to:NB to_read evolution_of_cooperation information_cascades social_influence sociology re:democratic_cognition macy.michael_w. no_youre_the_one_falsely_enforcing_an_unpopular_norm</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:041074ae3cfd/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:evolution_of_cooperation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:information_cascades"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_influence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:sociology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:democratic_cognition"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:macy.michael_w."/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:no_youre_the_one_falsely_enforcing_an_unpopular_norm"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.pnas.org/content/107/43/18375.short">
    <title>Spontaneous emergence of social influence in online systems | PNAS</title>
    <dc:date>2020-05-16T18:11:46+00:00</dc:date>
    <link>https://www.pnas.org/content/107/43/18375.short</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Social influence drives both offline and online human behavior. It pervades cultural markets, and manifests itself in the adoption of scientific and technical innovations as well as the spread of social practices. Prior empirical work on the diffusion of innovations in spatial regions or social networks has largely focused on the spread of one particular technology among a subset of all potential adopters. Here we choose an online context that allows us to study social influence processes by tracking the popularity of a complete set of applications installed by the user population of a social networking site, thus capturing the behavior of all individuals who can influence each other in this context. By extending standard fluctuation scaling methods, we analyze the collective behavior induced by 100 million application installations, and show that two distinct regimes of behavior emerge in the system. Once applications cross a particular threshold of popularity, social influence processes induce highly correlated adoption behavior among the users, which propels some of the applications to extraordinary levels of popularity. Below this threshold, the collective effect of social influence appears to vanish almost entirely, in a manner that has not been observed in the offline world. Our results demonstrate that even when external signals are absent, social influence can spontaneously assume an on–off nature in a digital environment. It remains to be seen whether a similar outcome could be observed in the offline world if equivalent experimental conditions could be replicated."]]></description>
<dc:subject>to:NB social_influence social_networks networked_life re:homophily_and_confounding color_me_skeptical</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:e038d2306f65/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_influence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:networked_life"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:homophily_and_confounding"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:color_me_skeptical"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://press.princeton.edu/books/hardcover/9780691199511/steadfast-democrats">
    <title>Steadfast Democrats | Princeton University Press</title>
    <dc:date>2020-03-12T14:56:50+00:00</dc:date>
    <link>https://press.princeton.edu/books/hardcover/9780691199511/steadfast-democrats</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Black Americans are by far the most unified racial group in American electoral politics, with 80 to 90 percent identifying as Democrats—a surprising figure given that nearly a third now also identify as ideologically conservative, up from less than 10 percent in the 1970s. Why has ideological change failed to push more black Americans into the Republican Party? Steadfast Democrats answers this question with a pathbreaking new theory that foregrounds the specificity of the black American experience and illuminates social pressure as the key element of black Americans’ unwavering support for the Democratic Party.
"Ismail White and Chryl Laird argue that the roots of black political unity were established through the adversities of slavery and segregation, when black Americans forged uniquely strong social bonds for survival and resistance. White and Laird explain how these tight communities have continued to produce and enforce political norms—including Democratic Party identification in the post–Civil Rights era. The social experience of race for black Americans is thus fundamental to their political choices. Black voters are uniquely influenced by the social expectations of other black Americans to prioritize the group’s ongoing struggle for freedom and equality. When navigating the choice of supporting a political party, this social expectation translates into affiliation with the Democratic Party. Through fresh analysis of survey data and original experiments, White and Laird explore where and how black political norms are enforced, what this means for the future of black politics, and how this framework can be used to understand the electoral behavior of other communities."]]></description>
<dc:subject>to:NB books:noted us_politics the_american_dilemma political_science social_influence</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:3f7940e14f05/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:books:noted"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:us_politics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:the_american_dilemma"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:political_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_influence"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://iopscience.iop.org/article/10.1088/1742-5468/2013/12/P12002">
    <title>Spreading dynamics in complex networks - IOPscience</title>
    <dc:date>2020-02-15T19:22:54+00:00</dc:date>
    <link>https://iopscience.iop.org/article/10.1088/1742-5468/2013/12/P12002</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Searching for influential spreaders in complex networks is an issue of great significance for applications across various domains, ranging from epidemic control, innovation diffusion, viral marketing, and social movement to idea propagation. In this paper, we first display some of the most important theoretical models that describe spreading processes, and then discuss the problem of locating both the individual and multiple influential spreaders respectively. Recent approaches in these two topics are presented. For the identification of privileged single spreaders, we summarize several widely used centralities, such as degree, betweenness centrality, PageRank, k-shell, etc. We investigate the empirical diffusion data in a large scale online social community—LiveJournal. With this extensive dataset, we find that various measures can convey very distinct information of nodes. Of all the users in the LiveJournal social network, only a small fraction of them are involved in spreading. For the spreading processes in LiveJournal, while degree can locate nodes participating in information diffusion with higher probability, k-shell is more effective in finding nodes with a large influence. Our results should provide useful information for designing efficient spreading strategies in reality."

--- Eh, the measure of "influence" is just the size of the reachable set.  (They don't actually track the dynamics of anything.)]]></description>
<dc:subject>networks re:do-institutions-evolve social_influence have_read in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:ebffd8b03897/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:do-institutions-evolve"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_influence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.jstor.org/stable/j.ctt7rgn0">
    <title>Heroes and Cowards: The Social Face of War on JSTOR</title>
    <dc:date>2020-01-26T18:09:46+00:00</dc:date>
    <link>https://www.jstor.org/stable/j.ctt7rgn0</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>to:NB books:noted social_influence war moral_psychology downloaded</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:5630c38b7b3c/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:books:noted"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_influence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:war"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:moral_psychology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:downloaded"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://journals.sagepub.com/doi/full/10.1177/2378023119868591">
    <title>Network Effects in Blau Space: Imputing Social Context from Survey Data - Miller McPherson, Jeffrey A. Smith, 2019</title>
    <dc:date>2020-01-09T22:04:36+00:00</dc:date>
    <link>https://journals.sagepub.com/doi/full/10.1177/2378023119868591</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We develop a method of imputing ego network characteristics for respondents in probability samples of individuals. This imputed network uses the homophily principle to estimate certain properties of a respondent’s core discussion network in the absence of actual network data. These properties measure the potential exposure of respondents to the attitudes, values, beliefs, and so on of their (likely) network alters. We use American National Election Study data to demonstrate that the imputed network features show substantial effects on individual-level measures, such as political attitudes and beliefs. In some cases, the imputed network variable substantially reduces the effects of standard sociodemographic variables, like age and education. We argue that the imputed network variable captures many of the aspects of social context that have been at the core of sociological analysis for decades."

--- Look, I'm 100% on board with the idea that lots of what social scientists regard as the effect of "sociodemographic variables" is really homophily + influence [http://bactra.org/notebooks/neutral-cultural-networks.html].  But since these effects are unidentified if you _have_ network data, how on Earth can you identify them if you have to impute the network?!?]]></description>
<dc:subject>to:NB to_read homophily sociology social_influence re:homophily_and_confounding missing_data inference_to_latent_objects color_me_skeptical</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:07d3b393c731/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:homophily"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:sociology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_influence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:homophily_and_confounding"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:missing_data"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:inference_to_latent_objects"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:color_me_skeptical"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://press.princeton.edu/ideas/what-do-you-really-know-about-gullibility">
    <title>What do you really know about gullibility? | Princeton University Press</title>
    <dc:date>2020-01-09T20:44:13+00:00</dc:date>
    <link>https://press.princeton.edu/ideas/what-do-you-really-know-about-gullibility</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>mercier.hugo cognitive_science psychology apparently_irrational_beliefs persuasion social_influence books:noted via:henry_farrell books:owned books:suggest_to_library</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:f7a07fa4ace3/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:mercier.hugo"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:cognitive_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:psychology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:apparently_irrational_beliefs"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:persuasion"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_influence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:books:noted"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:henry_farrell"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:books:owned"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:books:suggest_to_library"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://global.oup.com/academic/product/cultural-evolution-in-the-digital-age-9780198835943?cc=us&amp;lang=en&amp;#">
    <title>Cultural Evolution in the Digital Age - Alberto Acerbi - Oxford University Press</title>
    <dc:date>2020-01-09T20:42:15+00:00</dc:date>
    <link>https://global.oup.com/academic/product/cultural-evolution-in-the-digital-age-9780198835943?cc=us&amp;lang=en&amp;#</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["From emails to social media, from instant messaging to political memes, the way we produce and transmit culture is radically changing. Understanding the consequences of the massive diffusion of digital media is of the utmost importance, both from the intellectual and the social point of view.
"'Cultural Evolution in the Digital Age' proposes that a specific discipline - cultural evolution - provides an excellent framework to analyse our digital age. Cultural evolution is a vibrant, interdisciplinary, and increasingly productive scientific framework that aims to provide a naturalistic and quantitative explanation of culture. In the book the author shows how cultural evolution offers both a sophisticated view of human behaviour, grounded in cognitive science and evolutionary theory, and a strong quantitative and experimental methodology. The book examines in depth various topics that directly originate from the application of cultural evolution research to digital media.
"Is online social influence radically different from previous forms of social influence? Do digital media amplify the effects of popularity and celebrity influence? What are the psychological forces that favour the spread of online misinformation? What are the effects of the hyper-availability of information online on cultural cumulation? The cultural evolutionary perspective provides novel insights, and a relatively encouraging take on the overall effects of our online activities on our culture."]]></description>
<dc:subject>books:noted cultural_evolution social_influence social_life_of_the_mind social_media networked_life re:actually-dr-internet-is-the-name-of-the-monsters-creator in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:86b28a5b7cf1/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:books:noted"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:cultural_evolution"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_influence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_life_of_the_mind"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_media"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:networked_life"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:actually-dr-internet-is-the-name-of-the-monsters-creator"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://journals.sagepub.com/doi/abs/10.1177/0003122418797576">
    <title>Beyond Social Contagion: Associative Diffusion and the Emergence of Cultural Variation - Amir Goldberg, Sarah K. Stein, 2018</title>
    <dc:date>2019-11-11T00:15:16+00:00</dc:date>
    <link>https://journals.sagepub.com/doi/abs/10.1177/0003122418797576</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Network models of diffusion predominantly think about cultural variation as a product of social contagion. But culture does not spread like a virus. We propose an alternative explanation we call associative diffusion. Drawing on two insights from research in cognition—that meaning inheres in cognitive associations between concepts, and that perceived associations constrain people’s actions—we introduce a model in which, rather than beliefs or behaviors, the things being transmitted between individuals are perceptions about what beliefs or behaviors are compatible with one another. Conventional contagion models require the assumption that networks are segregated to explain cultural variation. We show, in contrast, that the endogenous emergence of cultural differentiation can be entirely attributable to social cognition and does not require a segregated network or a preexisting division into groups. Moreover, we show that prevailing assumptions about the effects of network topology do not hold when diffusion is associative."

--- Preprint version: https://web.stanford.edu/~amirgo/docs/beyond.pdf

(I'm not sure that this _is_ really an alternative explanation.  Or, rather, it would be an explanation for cultural polarization wtihin a densely-connected community, but not an explanation for associations between cultural traits and social identities.  Also, I think their conclusion that small-world networks lead to less "meaningful" cultural differentiation than do scale-free networks may be an artifact of the way they're using mutual information.  If there was one community and everyone in it enacted the same practices, they'd get an MI of 0, but that wouldn't make them meaningless....)]]></description>
<dc:subject>to:NB social_influence contagion homophily cultural_transmission cultural_differences sociology re:do-institutions-evolve have_read</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:f5b68cc828f8/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_influence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:contagion"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:homophily"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:cultural_transmission"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:cultural_differences"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:sociology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:do-institutions-evolve"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1909.12089">
    <title>[1909.12089] From classical to modern opinion dynamics</title>
    <dc:date>2019-10-01T16:34:09+00:00</dc:date>
    <link>https://arxiv.org/abs/1909.12089</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["In this age of Facebook, Instagram and Twitter, there is rapidly growing interest in understanding network-enabled opinion dynamics in large groups of autonomous agents. The phenomena of opinion polarization, the spread of propaganda and fake news, and the manipulation of sentiment are of interest to large numbers of organizations and people, some of whom are resource rich. Whether it is the more nefarious players such as foreign governments that are attempting to sway elections or large corporations that are trying to bend sentiment -- often quite surreptitiously, or it is more open and above board, like researchers that want to spread the news of some finding or some business interest that wants to make a large group of people aware of genuinely helpful innovations that they are marketing, what is at stake is often significant. In this paper we review many of the classical, and some of the new, social interaction models aimed at understanding opinion dynamics. While the first papers studying opinion dynamics appeared over 60 years ago, there is still a great deal of room for innovation and exploration. We believe that the political climate and the extraordinary (even unprecedented) events in the sphere of politics in the last few years will inspire new interest and new ideas. It is our aim to help those interested researchers understand what has already been explored in a significant portion of the field of opinion dynamics. We believe that in doing this, it will become clear that there is still much to be done."]]></description>
<dc:subject>to:NB social_influence</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:30b847027863/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_influence"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1909.11713">
    <title>[1909.11713] Strategic reciprocity improves academic performance in public elementary school children</title>
    <dc:date>2019-10-01T16:29:25+00:00</dc:date>
    <link>https://arxiv.org/abs/1909.11713</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Social networks are pivotal for learning. Yet, we still lack a full understanding of the mechanisms connecting networks with learning outcomes. Here, we present the results of a large scale study (946 elementary school children from 45 different classrooms) designed to understand the social strategies used by elementary school children. We mapped the social networks of students using both, a non-anonymous version of a prisoner's dilemma and a survey of nominated friendships, and compared the strategies played by students with their GPAs. We found that higher GPA students invest more strategically in their relationships, cooperating more generously with friends and less generously with non-friends than lower GPA students. Our findings suggest that the higher selectivity of social capital investments by high performing students may be one of the mechanisms helping them reap the learning benefits of their social networks."

--- This is confounded in so many different ways I hardly know where to start.]]></description>
<dc:subject>to:NB social_networks social_influence education color_me_skeptical</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:352c2a46e267/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_influence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:education"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:color_me_skeptical"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1909.12603">
    <title>[1909.12603] Tyranny to Anarchy: Regimes of Organisational Influence on Directed Hierarchical Graphs</title>
    <dc:date>2019-10-01T16:27:49+00:00</dc:date>
    <link>https://arxiv.org/abs/1909.12603</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Social organisational is critical to coordinated behaviour with a diverse range of management structures. In almost all organisations, a power structure exists with managers and subordinates. Often a change in one part can cause long-term cascades throughout the organisation, leading to inefficiencies and confusion. As organisations grow in size and complexity, as well as change the way they share information and power, we analyse how their resilience to disturbances is affected. Here, we consider majority rule dynamics on organisations modelled as hierarchical directed graphs, where the direction indicates task flow. We utilise a topological measure called the trophic incoherence parameter, q, which effectively gauges the stratification of power structure in an organisation. This is shown to bound regimes of behaviour. There is fast consensus at low q (e.g. tyranny), slow consensus at mid q (e.g. democracy), and no consensus at high q (e.g. anarchy). These regimes are investigated analytically and empirically with diverse case studies in the Roman Army, US Government, and a healthcare organisation. Our work has widespread application in the design of organisations as well as analysing how some become inefficient and stagnate."]]></description>
<dc:subject>to:NB social_networks social_influence re:democratic_cognition</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:bceea6d15d1e/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_influence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:democratic_cognition"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1909.10554">
    <title>[1909.10554] A data-driven model for Mass Media influence in electoral context</title>
    <dc:date>2019-09-26T18:04:30+00:00</dc:date>
    <link>https://arxiv.org/abs/1909.10554</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Mass Media outlets have occupied the central role of the political scenario, and are persuasive in the process of opinion formation of the citizens. In particular, the study of the relationship between Mass Media and behaviour of citizens can be monitored during election times, given the accessibility of news related to the candidates and polls that precede the election's day. In this paper we present a novel two-dimensional data driven Mass Media model based on semantic analysis of newspapers and national election surveys, which we use to analyse how a single influence mechanism should behave in order to reproduce the behaviour of the voters. Using simple and feasible rules for dynamics, we were able to find a notable agreement between the model's predictions and the polls which help us to understand the underlying mechanisms of the interactions between reader and media."]]></description>
<dc:subject>to:NB political_science social_influence statistics color_me_skeptical</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:686150063148/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:political_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_influence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:color_me_skeptical"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.nature.com/articles/s41586-019-1507-6">
    <title>Information gerrymandering and undemocratic decisions | Nature</title>
    <dc:date>2019-09-05T13:25:59+00:00</dc:date>
    <link>https://www.nature.com/articles/s41586-019-1507-6</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["People must integrate disparate sources of information when making decisions, especially in social contexts. But information does not always flow freely. It can be constrained by social networks1,2,3 and distorted by zealots and automated bots4. Here we develop a voter game as a model system to study information flow in collective decisions. Players are assigned to competing groups (parties) and placed on an ‘influence network’ that determines whose voting intentions each player can observe. Players are incentivized to vote according to partisan interest, but also to coordinate their vote with the entire group. Our mathematical analysis uncovers a phenomenon that we call information gerrymandering: the structure of the influence network can sway the vote outcome towards one party, even when both parties have equal sizes and each player has the same influence. A small number of zealots, when strategically placed on the influence network, can also induce information gerrymandering and thereby bias vote outcomes. We confirm the predicted effects of information gerrymandering in social network experiments with n = 2,520 human subjects. Furthermore, we identify extensive information gerrymandering in real-world influence networks, including online political discussions leading up to the US federal elections, and in historical patterns of bill co-sponsorship in the US Congress and European legislatures. Our analysis provides an account of the vulnerabilities of collective decision-making to systematic distortion by restricted information flow. Our analysis also highlights a group-level social dilemma: information gerrymandering can enable one party to sway decisions in its favour, but when multiple parties engage in gerrymandering the group loses its ability to reach consensus and remains trapped in deadlock."]]></description>
<dc:subject>to:NB social_influence social_networks collective_cognition re:do-institutions-evolve re:democratic_cognition via:rvenkat to_read</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:0e10d5cc218d/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_influence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:collective_cognition"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:do-institutions-evolve"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:democratic_cognition"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:rvenkat"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1908.10129">
    <title>[1908.10129] Network Communities of Dynamical Influence</title>
    <dc:date>2019-08-29T00:49:13+00:00</dc:date>
    <link>https://arxiv.org/abs/1908.10129</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Fuelled by a desire for greater connectivity, networked systems now pervade our society at an unprecedented level that will affect it in ways we do not yet understand. Nature, in contrast, has already developed efficient and resilient large-scale networks, including brain connectomes and bird flocks. These natural systems rely on the stimulation of key elements, which access effective pathways of communication, to instigate response and consensus. In this paper, we explore the link between network structure and dynamical influence to further our understanding of these effective networks. Our technique identifies key vertices that rapidly drive a network to consensus, and the communities that form under their dynamical influence, by investigating the relationships between the system's dominant eigenvectors. These communities of dynamical influence enable the clear identification of human subjects from their brain connectomes and provides an insight into functional activity. They are also used to highlighted the effectiveness of starling flocks, where increasing the outdegree is likely to produce a less responsive flock that has the most influential birds poorly positioned to observe a predator and, hence, instigate an evasion manoeuvre."]]></description>
<dc:subject>to:NB social_influence community_discovery network_data_analysis color_me_skeptical</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:22e81081c017/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_influence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:community_discovery"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:network_data_analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:color_me_skeptical"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1906.10258">
    <title>[1906.10258] Policy Targeting under Network Interference</title>
    <dc:date>2019-08-29T00:46:59+00:00</dc:date>
    <link>https://arxiv.org/abs/1906.10258</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["This paper discusses the problem of estimating individualized treatment allocation rules under network interference. We propose a method with several appealing features for applications: we let treatment and spillover effects be heterogeneous in the population, and we construct targeting rules that exploit such heterogeneity; we accommodate for arbitrary, possibly non-linear, regression models, and we propose estimators that are robust to model misspecification; treatment allocation rules depend on an arbitrary set of individual, neighbors' and network characteristics, and we allow for general constraints on the policy function and capacity constraints on the number of treated units; the proposed methodology is valid also when only local information of the network is observed. From a theoretical perspective, we establish the first set of guarantees on the utilitarian regret under interference, and we show that it achieves the min-max optimal rate in scenarios of practical and theoretical interest. We provide a mixed-integer linear program formulation of the optimization problem, that can be solved using standard optimization routines. We discuss the empirical performance in simulations, and we illustrate our method by investigating the role of social networks in micro-finance decisions."]]></description>
<dc:subject>to:NB social_influence causal_inference network_data_analysis statistics econometrics</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:d30fddcdd222/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_influence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:causal_inference"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:network_data_analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:econometrics"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://journals.aps.org/pre/abstract/10.1103/PhysRevE.100.022305">
    <title>Phys. Rev. E 100, 022305 (2019) - Social reinforcement with weighted interactions</title>
    <dc:date>2019-08-10T01:27:50+00:00</dc:date>
    <link>https://journals.aps.org/pre/abstract/10.1103/PhysRevE.100.022305</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["The speed and extent of diffusion of behaviors in social networks depends on network structure and individual preferences. The contribution of the present study is twofold. First, we introduce weighted interactions between potential adopters that depend on the similarity in their preferences and moderate the strength of social reinforcement. The reason for the extension is the existence of a confirmation bias in the way agents treat information by prioritizing evidence conforming to their opinion. As a result, individuals become less likely to be influenced by peers with relatively different preferences, reducing the overall diffusion rate under clustered networks. Second, we enrich our analysis by also considering a scale free network topology with a high degree asymmetry, motivated by its pervasiveness in online social networks. This network performs consistently well in terms of diffusion for different parameter combinations and clearly outperforms clustered networks under weighted interactions. Our results show that more realistic assumptions regarding agents' interactions shift the focus from clustering to degree distribution in the study of network structures allowing for fast and widespread behavior adoption."]]></description>
<dc:subject>to:NB social_networks voter_model social_influence re:do-institutions-evolve</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:b66e3acc6869/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:voter_model"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_influence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:do-institutions-evolve"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1709.10024">
    <title>[1709.10024] Estimation of Peer Effects in Endogenous Social Networks: Control Function Approach</title>
    <dc:date>2019-08-01T16:15:20+00:00</dc:date>
    <link>https://arxiv.org/abs/1709.10024</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We propose a method of estimating the linear-in-means model of peer effects in which the peer group, defined by a social network, is endogenous in the outcome equation for peer effects. Endogeneity is due to unobservable individual characteristics that influence both link formation in the network and the outcome of interest. We propose two estimators of the peer effect equation that control for the endogeneity of the social connections using a control function approach. We leave the functional form of the control function unspecified and treat it as unknown. To estimate the model, we use a sieve semiparametric approach, and we establish asymptotics of the semiparametric estimator."]]></description>
<dc:subject>to:NB causal_inference social_influence re:homophily_and_confounding social_networks</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:8d43ded3a813/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:causal_inference"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_influence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:homophily_and_confounding"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_networks"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1906.09076">
    <title>[1906.09076] Inside the Echo Chamber: Disentangling network dynamics from polarization</title>
    <dc:date>2019-06-25T13:28:23+00:00</dc:date>
    <link>https://arxiv.org/abs/1906.09076</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Echo chambers are defined by the simultaneous presence of opinion polarization with respect to a controversial topic and homophily, i.e. the preference of individuals to interact with like-minded peers. While recent efforts have been devoted to detecting the presence of echo chambers in polarized debates on online social media, the dynamics leading to the emergence of these phenomena remain unclear. Here, we contribute to this endeavor by proposing novel metrics to single out the effect of the network dynamics from the opinion polarization. By using a Twitter data set collected during a controversial political debate in Brazil in 2016, we employ a temporal network approach to gauge the strength of the echo chamber effect over time. We define a measure of opinion coherence in the network showing how the echo chamber becomes weaker across the observed period. The analysis of the hashtags diffusion in the network shows that this is due to the increase of social interactions between users with opposite opinions. Finally, the analysis of the mutual entropy between the opinions expressed and received by the users permits to quantify the social contagion effect. We find empirical evidence that the polarization of the users and the dynamics of their interactions may evolve independently. Our findings may be of interest to the broad array of researchers studying the dynamics of echo chambers and polarization in online social networks."]]></description>
<dc:subject>to:NB social_media social_life_of_the_mind social_influence re:homophily_and_confounding</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:2de519b626f1/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_media"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_life_of_the_mind"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_influence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:homophily_and_confounding"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1906.08772">
    <title>[1906.08772] Understanding Filter Bubbles and Polarization in Social Networks</title>
    <dc:date>2019-06-21T19:32:00+00:00</dc:date>
    <link>https://arxiv.org/abs/1906.08772</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Recent studies suggest that social media usage -- while linked to an increased diversity of information and perspectives for users -- has exacerbated user polarization on many issues. A popular theory for this phenomenon centers on the concept of "filter bubbles": by automatically recommending content that a user is likely to agree with, social network algorithms create echo chambers of similarly-minded users that would not have arisen otherwise. However, while echo chambers have been observed in real-world networks, the evidence for filter bubbles is largely post-hoc. 
"In this work, we develop a mathematical framework to study the filter bubble theory. We modify the classic Friedkin-Johnsen opinion dynamics model by introducing another actor, the network administrator, who filters content for users by making small changes to the edge weights of a social network (for example, adjusting a news feed algorithm to change the level of interaction between users). 
"On real-world networks from Reddit and Twitter, we show that when the network administrator is incentivized to reduce disagreement among users, even relatively small edge changes can result in the formation of echo chambers in the network and increase user polarization. We theoretically support this observed sensitivity of social networks to outside intervention by analyzing synthetic graphs generated from the stochastic block model. Finally, we show that a slight modification to the incentives of the network administrator can mitigate the filter bubble effect while minimally affecting the administrator's target objective, user disagreement."]]></description>
<dc:subject>to:NB social_influence social_media networked_life re:actually-dr-internet-is-the-name-of-the-monsters-creator</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:21696929c9c0/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_influence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_media"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:networked_life"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:actually-dr-internet-is-the-name-of-the-monsters-creator"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.tandfonline.com/doi/full/10.1080/01621459.2019.1617153">
    <title>Testing and Estimation of Social Network Dependence With Time to Event Data: Journal of the American Statistical Association: Vol 0, No 0</title>
    <dc:date>2019-06-21T15:46:31+00:00</dc:date>
    <link>https://www.tandfonline.com/doi/full/10.1080/01621459.2019.1617153</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Nowadays, events are spread rapidly along social networks. We are interested in whether people’s responses to an event are affected by their friends’ characteristics. For example, how soon will a person start playing a game given that his/her friends like it? Studying social network dependence is an emerging research area. In this work, we propose a novel latent spatial autocorrelation Cox model to study social network dependence with time-to-event data. The proposed model introduces a latent indicator to characterize whether a person’s survival time might be affected by his or her friends’ features. We first propose a score-type test for detecting the existence of social network dependence. If it exists, we further develop an EM-type algorithm to estimate the model parameters. The performance of the proposed test and estimators are illustrated by simulation studies and an application to a time-to-event dataset about playing a popular mobile game from one of the largest online social network platforms. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement."]]></description>
<dc:subject>to:NB network_data_analysis survival_analysis statistics social_influence re:homophily_and_confounding</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:5a200054439c/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:network_data_analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:survival_analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_influence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:homophily_and_confounding"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.annualreviews.org/doi/full/10.1146/annurev-criminol-011518-024551">
    <title>Peer Influence and Delinquency | Annual Review of Criminology</title>
    <dc:date>2019-05-26T18:17:04+00:00</dc:date>
    <link>https://www.annualreviews.org/doi/full/10.1146/annurev-criminol-011518-024551</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Peer influence occupies an intriguing place in criminology. On the one hand, there is a long line of theorizing and empirical work highlighting it as a key causal process for delinquency. On the other, there is a group of theoretical skeptics who view it as one of the most notorious examples of a spurious link. After discussing these perspectives, this review takes stock of our intellectual advancements in understanding peer influence over decades' worth of research toward this endeavor. We conclude that although there have been important gains, essential questions and gaps remain. Toward this aim, we offer some lines of future work that we believe offer pathways to yielding the greatest added value to the discipline."]]></description>
<dc:subject>to:NB social_influence crime re:homophily_and_confounding</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:1f9a6da361be/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_influence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:crime"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:homophily_and_confounding"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://journals.sagepub.com/doi/10.1177/0081175018820075">
    <title>Social Space Diffusion: Applications of a Latent Space Model to Diffusion with Uncertain Ties - Jacob C. Fisher, 2019</title>
    <dc:date>2019-02-09T14:44:13+00:00</dc:date>
    <link>https://journals.sagepub.com/doi/10.1177/0081175018820075</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Social networks represent two different facets of social life: (1) stable paths for diffusion, or the spread of something through a connected population, and (2) random draws from an underlying social space, which indicate the relative positions of the people in the network to one another. The dual nature of networks creates a challenge: if the observed network ties are a single random draw, is it realistic to expect that diffusion only follows the observed network ties? This study takes a first step toward integrating these two perspectives by introducing a social space diffusion model. In the model, network ties indicate positions in social space, and diffusion occurs proportionally to distance in social space. Practically, the simulation occurs in two parts. First, positions are estimated using a statistical model (in this example, a latent space model). Then, second, the predicted probabilities of a tie from that model—representing the distances in social space—or a series of networks drawn from those probabilities—representing routine churn in the network—are used as weights in a weighted averaging framework. Using longitudinal data from high school friendship networks, the author explores the properties of the model. The author shows that the model produces smoothed diffusion results, which predict attitudes in future waves 10 percent better than a diffusion model using the observed network and up to 5 percent better than diffusion models using alternative, non-model-based smoothing approaches."]]></description>
<dc:subject>to:NB to_read social_influence social_networks network_data_analysis re:homophily_and_confounding to_teach:baby-nets via:gabriel_rossman</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:5088623e657d/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_influence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:network_data_analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:homophily_and_confounding"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:baby-nets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:gabriel_rossman"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://onlinelibrary.wiley.com/doi/book/10.1002/9781118833162">
    <title>Political Attitudes | Wiley Online Books</title>
    <dc:date>2019-01-07T17:36:48+00:00</dc:date>
    <link>https://onlinelibrary.wiley.com/doi/book/10.1002/9781118833162</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Political Science has traditionally employed empirical research and analytical resources to understand, explain and predict political phenomena. One of the long-standing criticisms against empirical modeling targets the static perspective provided by the model-invariant paradigm. In political science research, this issue has a particular relevance since political phenomena prove sophisticated degrees of context-dependency whose complexity could be hardly captured by traditional approaches. To cope with the complexity challenge, a new modeling paradigm was needed. This book is concerned with this challenge. Moreover, the book aims to reveal the power of computational modeling of political attitudes to reinforce the political methodology in facing two fundamental challenges: political culture modeling and polity modeling. The book argues that an artificial polity model as a powerful research instrument could hardly be effective without the political attitude and, by extension, the political culture computational and simulation modeling theory, experiments and practice."]]></description>
<dc:subject>to:NB books:noted downloaded voter_model social_influence public_opinion agent-based_models political_science simulation interacting_particle_systems</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:741a3f44ef53/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:books:noted"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:downloaded"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:voter_model"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_influence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:public_opinion"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:agent-based_models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:political_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:simulation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:interacting_particle_systems"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.mitpressjournals.org/doi/abs/10.1162/rest_a_00716">
    <title>Optimal Design of Experiments in the Presence of Interference | The Review of Economics and Statistics | MIT Press Journals</title>
    <dc:date>2019-01-04T03:26:30+00:00</dc:date>
    <link>https://www.mitpressjournals.org/doi/abs/10.1162/rest_a_00716</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We formalize the optimal design of experiments when there is interference between units, that is, an individual’s outcome depends on the outcomes of others in her group. We focus on randomized saturation designs, two-stage experiments that first randomize treatment saturation of a group, then individual treatment assignment. We map the potential outcomes framework with partial interference to a regression model with clustered errors, calculate standard errors of randomized saturation designs, and derive analytical insights about the optimal design. We show that the power to detect average treatment effects declines precisely with the ability to identify novel treatment and spillover effects."]]></description>
<dc:subject>to:NB to_read experimental_design network_data_analysis social_influence re:do_not_adjust_your_receiver</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:deb1dae90f27/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:experimental_design"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:network_data_analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_influence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:do_not_adjust_your_receiver"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1811.10372">
    <title>[1811.10372] Disentangling sources of influence in online social networks</title>
    <dc:date>2018-12-23T21:48:57+00:00</dc:date>
    <link>https://arxiv.org/abs/1811.10372</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Information propagation in online social networks is facilitated by two types of influence - endogenous (peer) influence that is dependent on the network structure and current state of each user and exogenous (external) which is independent of these. However, inference of these influences from data remains a challenge. In this paper we propose a methodology that yields estimates of both endogenous and exogenous influence using only a social network structure and a single activation cascade. We evaluate our methodology on simulated activation cascades as well as on cascades obtained from several large Facebook political survey applications. We show that our methodology is able to provide estimates of endogenous and exogenous influence in online social networks, characterize activation of each individual user as being endogenously or exogenously driven, and to identify most influential groups of users."]]></description>
<dc:subject>to:NB to_read contagion social_influence re:homophily_and_confounding color_me_skeptical</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:82de02b92b54/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:contagion"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_influence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:homophily_and_confounding"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:color_me_skeptical"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1809.10302">
    <title>[1809.10302] The hidden traits of endemic illiteracy in cities</title>
    <dc:date>2018-09-30T13:34:49+00:00</dc:date>
    <link>https://arxiv.org/abs/1809.10302</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["In spite of the considerable progress towards reducing illiteracy rates, many countries, including developed ones, have encountered difficulty achieving further reduction in these rates. This is worrying because illiteracy has been related to numerous health, social, and economic problems. Here, we show that the spatial patterns of illiteracy in urban systems have several features analogous to the spread of diseases such as dengue and obesity. Our results reveal that illiteracy rates are spatially long-range correlated, displaying non-trivial clustering structures characterized by percolation-like transitions and fractality. These patterns can be described in the context of percolation theory of long-range correlated systems at criticality. Together, these results provide evidence that the illiteracy incidence can be related to a transmissible process, in which the lack of access to minimal education propagates in a population in a similar fashion to endemic diseases."

--- Of course it's coming out in _Physica A_.]]></description>
<dc:subject>to:NB sociology social_influence re:homophily_and_confounding color_me_skeptical</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:34e58af9730e/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:sociology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_influence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:homophily_and_confounding"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:color_me_skeptical"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.aeaweb.org/articles?id=10.1257/aer.20141708">
    <title>Estimating Group Effects Using Averages of Observables to Control for Sorting on Unobservables: School and Neighborhood Effects</title>
    <dc:date>2018-09-27T16:33:19+00:00</dc:date>
    <link>https://www.aeaweb.org/articles?id=10.1257/aer.20141708</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We consider the classic problem of estimating group treatment effects when individuals sort based on observed and unobserved characteristics. Using a standard choice model, we show that controlling for group averages of observed individual characteristics potentially absorbs all the across-group variation in unobservable individual characteristics. We use this insight to bound the treatment effect variance of school systems and associated neighborhoods for various outcomes. Across multiple datasets, we find that a 90th versus 10th percentile school/neighborhood increases the high school graduation probability and college enrollment probability by at least 0.04 and 0.11 and permanent wages by 13.7 percent."

--- This sounds bogus, or at least question-begging, to me.  ("If you assume that unobserved characteristics can be perfectly tracked by observed characteristics, you don't need to worry about unobserved characteristics" --- not an actual quote.)  The last tag applies.]]></description>
<dc:subject>to:NB social_influence causal_inference color_me_skeptical</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:5eea75b80676/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_influence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:causal_inference"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:color_me_skeptical"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://press.princeton.edu/titles/11279.html">
    <title>Centola, D.: How Behavior Spreads: The Science of Complex Contagions (Hardcover and eBook) | Princeton University Press</title>
    <dc:date>2018-07-02T19:35:15+00:00</dc:date>
    <link>https://press.princeton.edu/titles/11279.html</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["New social movements, technologies, and public-health initiatives often struggle to take off, yet many diseases disperse rapidly without issue. Can the lessons learned from the viral diffusion of diseases be used to improve the spread of beneficial behaviors and innovations? In How Behavior Spreads, Damon Centola presents over a decade of original research examining how changes in societal behavior--in voting, health, technology, and finance—occur and the ways social networks can be used to influence how they propagate. Centola's startling findings show that the same conditions accelerating the viral expansion of an epidemic unexpectedly inhibit the spread of behaviors.
"While it is commonly believed that "weak ties"—long-distance connections linking acquaintances—lead to the quicker spread of behaviors, in fact the exact opposite holds true. Centola demonstrates how the most well-known, intuitive ideas about social networks have caused past diffusion efforts to fail, and how such efforts might succeed in the future. Pioneering the use of Web-based methods to understand how changes in people's social networks alter their behaviors, Centola illustrates the ways in which these insights can be applied to solve countless problems of organizational change, cultural evolution, and social innovation. His findings offer important lessons for public health workers, entrepreneurs, and activists looking to harness networks for social change."]]></description>
<dc:subject>books:noted contagion social_influence social_networks centola.damon sociology re:homophily_and_confounding books:owned in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:201162708c94/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:books:noted"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:contagion"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_influence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:centola.damon"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:sociology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:homophily_and_confounding"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:books:owned"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
</rdf:RDF>