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    <title>Pinboard (cshalizi)</title>
    <link>https://pinboard.in/u:cshalizi/public/</link>
    <description>recent bookmarks from cshalizi</description>
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	<rdf:li rdf:resource="https://arxiv.org/abs/1908.06173"/>
	<rdf:li rdf:resource="http://mitpress.mit.edu/books/spam"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1206.4675"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1206.4637"/>
	<rdf:li rdf:resource="http://www.wired.com/beyond_the_beyond/2012/02/the-difference-engine-is-almost-grown-with-growing-appropriate-arc-of-clowns/"/>
	<rdf:li rdf:resource="http://recaptcha.net/"/>
	<rdf:li rdf:resource="http://williamtozier.com/slurry/2005/10/16/wseas-and-their-diabolical-pyramid-scheme-of-never-ending-spam"/>
	<rdf:li rdf:resource="http://sparrow.ece.cmu.edu/group/pub/franklin_paxson_perrig_savage_miscreants.pdf"/>
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  </channel><item rdf:about="https://arxiv.org/abs/1212.6806">
    <title>[1212.6806] Leveraging Sociological Models for Predictive Analytics</title>
    <dc:date>2019-09-29T13:41:45+00:00</dc:date>
    <link>https://arxiv.org/abs/1212.6806</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["There is considerable interest in developing techniques for predicting human behavior, for instance to enable emerging contentious situations to be forecast or the nature of ongoing but hidden activities to be inferred. A promising approach to this problem is to identify and collect appropriate empirical data and then apply machine learning methods to these data to generate the predictions. This paper shows the performance of such learning algorithms often can be improved substantially by leveraging sociological models in their development and implementation. In particular, we demonstrate that sociologically-grounded learning algorithms outperform gold-standard methods in three important and challenging tasks: 1.) inferring the (unobserved) nature of relationships in adversarial social networks, 2.) predicting whether nascent social diffusion events will go viral, and 3.) anticipating and defending future actions of opponents in adversarial settings. Significantly, the new algorithms perform well even when there is limited data available for their training and execution."]]></description>
<dc:subject>to:NB sociology data_mining to_teach:data-mining spam</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:6ef2f1c9460e/</dc:identifier>
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	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:sociology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:data_mining"/>
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	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:spam"/>
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<item rdf:about="https://arxiv.org/abs/1908.06173">
    <title>[1908.06173] The History of Digital Spam</title>
    <dc:date>2019-08-20T14:17:44+00:00</dc:date>
    <link>https://arxiv.org/abs/1908.06173</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Spam!: that's what Lorrie Faith Cranor and Brian LaMacchia exclaimed in the title of a popular call-to-action article that appeared twenty years ago on Communications of the ACM. And yet, despite the tremendous efforts of the research community over the last two decades to mitigate this problem, the sense of urgency remains unchanged, as emerging technologies have brought new dangerous forms of digital spam under the spotlight. Furthermore, when spam is carried out with the intent to deceive or influence at scale, it can alter the very fabric of society and our behavior. In this article, I will briefly review the history of digital spam: starting from its quintessential incarnation, spam emails, to modern-days forms of spam affecting the Web and social media, the survey will close by depicting future risks associated with spam and abuse of new technologies, including Artificial Intelligence (e.g., Digital Humans). After providing a taxonomy of spam, and its most popular applications emerged throughout the last two decades, I will review technological and regulatory approaches proposed in the literature, and suggest some possible solutions to tackle this ubiquitous digital epidemic moving forward."]]></description>
<dc:subject>to:NB spam advertising deceiving_us_has_become_an_industrial_process history_of_technology history_of_computing networked_life to_teach:data-mining</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:fc28f62e9fad/</dc:identifier>
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	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:advertising"/>
	<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:history_of_technology"/>
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<item rdf:about="http://mitpress.mit.edu/books/spam">
    <title>Spam | The MIT Press</title>
    <dc:date>2014-11-20T00:22:23+00:00</dc:date>
    <link>http://mitpress.mit.edu/books/spam</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["The vast majority of all email sent every day is spam, a variety of idiosyncratically spelled requests to provide account information, invitations to spend money on dubious products, and pleas to send cash overseas. Most of it is caught by filters before ever reaching an in-box. Where does it come from? As Finn Brunton explains in Spam, it is produced and shaped by many different populations around the world: programmers, con artists, bots and their botmasters, pharmaceutical merchants, marketers, identity thieves, crooked bankers and their victims, cops, lawyers, network security professionals, vigilantes, and hackers. Every time we go online, we participate in the system of spam, with choices, refusals, and purchases the consequences of which we may not understand.
"This is a book about what spam is, how it works, and what it means. Brunton provides a cultural history that stretches from pranks on early computer networks to the construction of a global criminal infrastructure. The history of spam, Brunton shows us, is a shadow history of the Internet itself, with spam emerging as the mirror image of the online communities it targets. Brunton traces spam through three epochs: the 1970s to 1995, and the early, noncommercial computer networks that became the Internet; 1995 to 2003, with the dot-com boom, the rise of spam’s entrepreneurs, and the first efforts at regulating spam; and 2003 to the present, with the war of algorithms—spam versus anti-spam. Spam shows us how technologies, from email to search engines, are transformed by unintended consequences and adaptations, and how online communities develop and invent governance for themselves."]]></description>
<dc:subject>books:noted internet spam networked_life in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:5938631283dd/</dc:identifier>
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	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:internet"/>
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<item rdf:about="http://arxiv.org/abs/1206.4675">
    <title>[1206.4675] Finding Botnets Using Minimal Graph Clusterings</title>
    <dc:date>2012-06-23T14:14:58+00:00</dc:date>
    <link>http://arxiv.org/abs/1206.4675</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We study the problem of identifying botnets and the IP addresses which they comprise, based on the observation of a fraction of the global email spam traffic. Observed mailing campaigns constitute evidence for joint botnet membership, they are represented by cliques in the graph of all messages. No evidence against an association of nodes is ever available. We reduce the problem of identifying botnets to a problem of finding a minimal clustering of the graph of messages. We directly model the distribution of clusterings given the input graph; this avoids potential errors caused by distributional assumptions of a generative model. We report on a case study in which we evaluate the model by its ability to predict the spam campaign that a given IP address is going to participate in."]]></description>
<dc:subject>to:NB network_data_analysis spam networked_life</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:f04fce120c4b/</dc:identifier>
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	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:spam"/>
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</item>
<item rdf:about="http://arxiv.org/abs/1206.4637">
    <title>[1206.4637] Learning to Identify Regular Expressions that Describe Email Campaigns</title>
    <dc:date>2012-06-23T13:57:13+00:00</dc:date>
    <link>http://arxiv.org/abs/1206.4637</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["This paper addresses the problem of inferring a regular expression from a given set of strings that resembles, as closely as possible, the regular expression that a human expert would have written to identify the language. This is motivated by our goal of automating the task of postmasters of an email service who use regular expressions to describe and blacklist email spam campaigns. Training data contains batches of messages and corresponding regular expressions that an expert postmaster feels confident to blacklist. We model this task as a learning problem with structured output spaces and an appropriate loss function, derive a decoder and the resulting optimization problem, and a report on a case study conducted with an email service."]]></description>
<dc:subject>text_mining grammar_induction spam networked_life machine_learning in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:03a34947e812/</dc:identifier>
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</item>
<item rdf:about="http://www.wired.com/beyond_the_beyond/2012/02/the-difference-engine-is-almost-grown-with-growing-appropriate-arc-of-clowns/">
    <title>The difference engine is almost grown with growing appropriate arc of clowns. | Beyond The Beyond | Wired.com</title>
    <dc:date>2012-02-10T02:43:37+00:00</dc:date>
    <link>http://www.wired.com/beyond_the_beyond/2012/02/the-difference-engine-is-almost-grown-with-growing-appropriate-arc-of-clowns/</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Served by the refrigerator’s dose and university, eisenhower favored him if there was any television he could give him in his bowls."]]></description>
<dc:subject>spam sterling.bruce</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:812dd7a139c5/</dc:identifier>
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	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:sterling.bruce"/>
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<item rdf:about="http://recaptcha.net/">
    <title>reCAPTCHA: Stop Spam, Read Books</title>
    <dc:date>2008-10-01T17:34:55+00:00</dc:date>
    <link>http://recaptcha.net/</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>spam pattern_recognition carnegie_mellon</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:5746e3687018/</dc:identifier>
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</item>
<item rdf:about="http://williamtozier.com/slurry/2005/10/16/wseas-and-their-diabolical-pyramid-scheme-of-never-ending-spam">
    <title>Notional Slurry » WSEAS and their diabolical pyramid scheme of never-ending spam</title>
    <dc:date>2008-01-19T14:10:27+00:00</dc:date>
    <link>http://williamtozier.com/slurry/2005/10/16/wseas-and-their-diabolical-pyramid-scheme-of-never-ending-spam</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[The stupid spammers at WSEAS resort to legal action force Bill to take down a perfectly correct post about how they are, in fact, spamming idiots.
]]></description>
<dc:subject>spam suppressive_lawsuits why_oh_why_cant_we_have_a_better_academic_publishing_system WSEAS why_oh_why_cant_we_have_a_better_legal_system</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:0b94f21beb75/</dc:identifier>
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	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:suppressive_lawsuits"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:why_oh_why_cant_we_have_a_better_academic_publishing_system"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:WSEAS"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:why_oh_why_cant_we_have_a_better_legal_system"/>
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</item>
<item rdf:about="http://sparrow.ece.cmu.edu/group/pub/franklin_paxson_perrig_savage_miscreants.pdf">
    <title>An Inquiry into the Nature and Causes of the Wealth of Internet Miscreants</title>
    <dc:date>2007-10-29T20:12:32+00:00</dc:date>
    <link>http://sparrow.ece.cmu.edu/group/pub/franklin_paxson_perrig_savage_miscreants.pdf</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[Some measurements of an on-line marketplace (IRC channel) relating to electronic crimes, with SVMs to label "semantic" features
]]></description>
<dc:subject>content_analysis fraud text_mining data_mining spam economics via:schneier carnegie_mellon</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:c9d13f3cf978/</dc:identifier>
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	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:data_mining"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:spam"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:economics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:schneier"/>
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</item>
<item rdf:about="http://medicine.plosjournals.org/perlserv/?request=get-document&amp;doi=10.1371/journal.pmed.0040274">
    <title>PLoS Medicine - Will Spam Overwhelm Our Defenses? Evaluating Offerings for Drugs and Natural Health Products</title>
    <dc:date>2007-10-17T02:46:09+00:00</dc:date>
    <link>http://medicine.plosjournals.org/perlserv/?request=get-document&amp;doi=10.1371/journal.pmed.0040274</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["It was not easy to get approval from university administrators for a project that could include ordering products from unknown and unverified sources, and which could lead to illegal trade of substances, or costs associated with items for the enhancement
]]></description>
<dc:subject>spam via:danny-yee funny:academic</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:ae9042d3c5b7/</dc:identifier>
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