Suspended accounts in retrospect: an analysis of twitter spam

By Kurt Thomas, Chris Grier, Dawn Song, Vern Paxson. 2011

535 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications, Information Systems

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Citations in the corpus

Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019, methodology): “For instance, network studies of peer inﬂuence and social contagion suﬀer from a stubborn challenge of disambiguating such eﬀects from homophily among peers and within communities (Christakis and Fowler, 2007; Lyons, 2011; Shalizi and Thomas, 2011).”
Serf and turf: crowdturfing for fun and profit (2011, background): “Categories and Subject Descriptors H.3.5 [Information Storage and Retrieval]: Online Information Services-Web-based services; J.4 [Computer Applications]: Social and Behavioral Sciences General Terms Measurement, Security, Economics Keywords Crowdturfing, Crowdsourcing, Spam, Sybils, Experimentation”
Sampling Content from Online Social Networks (2015, background): “A number of recent studies have highlighted the growing problem with spam in Twitter [Grier et al. 2010; Thomas et al. 2011].”
Man vs. Machine: Practical Adversarial Detection of Malicious Crowdsourcing Workers (2014, background): “on spam detection [4, 50, 43], make it possible for adversaries to make reasonable guesses on the feature space.”
Understanding and combating link farming in the twitter social network (2012, methodology): “[27] studied the tools used by spam-accounts that were identified from among the accounts that were suspended by Twitter.”
The Tweets They Are a-Changin: Evolution of Twitter Users and Behavior (2014, background): “The rise of tweets unavailable due to suspended and deleted users corresponds strongly with the increase of spam on Twitter (Thomas et al. 2011).”
Exploiting Temporal Dynamics in Sybil Defenses (2015, background): “In recent years, however, there has been an emergence of an underground market that specializes in bypassing registration barriers such as email, phone, and CAPTCHA con-814 ﬁrmation [33, 23, 21].”
Aiding the Detection of Fake Accounts in Large Scale Social Online Services (2012, background): “In response to the inapplicability of automated account suspension, OSNs employ CAPTCHA [5] and photobased social authentication [13] to rate-limit suspected users, or manually inspect the features of accounts reported as abusive (flagged) by other users [12, 51, 55].”
Combating Attacks and Abuse in Large Online Communities (2016, methodology): “To begin with, we examine the possibility to buildmachine learning classifiers to detect workers who perform crowdturfing tasks (on Weibo, Chinese Twitter).”
Twitter's Glass Ceiling: The Effect of Perceived Gender on Online Visibility (2016, methodology): “1, following a previously suggested spam-filtering procedure (Thomas et al. 2011).”
Adapting Social Spam Infrastructure for Political Censorship (2012, background): “Despite the complexity of the attack, we show how Twitter’s relevance-based search helped mitigate the attack’s impact on users searching for information regarding the Russian election.”
Fame for sale: Efficient detection of fake Twitter followers (2015, background): “, in [27]) nor on correlations between accounts on different social platforms (as done, e.”
Detecting Automation of Twitter Accounts: Are You a Human, Bot, or Cyborg? (2012, background): “Fighting against spam on Twitter has been investigated in recent works [14], [22], [23].”