Badly Evolved? Exploring Long-Surviving Suspicious Users on Twitter

We study the behavior of long-lived eventually suspended accounts in social media through a comprehensive investigation of Arabic Twitter. With a threefold study of (i) the content these accounts post; (ii) the evolution of their linguistic patterns; and (iii) their activity evolution, we compare long-lived users versus short-lived, legitimate, and pro-ISIS users. We find that these long-lived accounts – though trying to appear normal – do exhibit significantly different behaviors from both normal and other suspended users. We additionally identify temporal changes and assess their value in supporting discovery of these accounts and find out that most accounts have actually being “hiding in plain sight” and are detectable early in their lifetime. Finally, we successfully apply our findings to address a series of classification tasks, most notably to determine whether a given account is a long-surviving account.

[1]  Barbara Poblete,et al.  Information credibility on twitter , 2011, WWW.

[2]  Eni Mustafaraj,et al.  From Obscurity to Prominence in Minutes: Political Speech and Real-Time Search , 2010 .

[3]  Huan Liu,et al.  The fragility of Twitter social networks against suspended users , 2015, 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM).

[4]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[5]  Michael J. Franklin,et al.  Resilient Distributed Datasets: A Fault-Tolerant Abstraction for In-Memory Cluster Computing , 2012, NSDI.

[6]  Chao Yang,et al.  Empirical Evaluation and New Design for Fighting Evolving Twitter Spammers , 2011, IEEE Transactions on Information Forensics and Security.

[7]  J. M. Berger,et al.  The ISIS Twitter census: defining and describing the population of ISIS supporters on Twitter , 2015 .

[8]  Kyumin Lee,et al.  Uncovering social spammers: social honeypots + machine learning , 2010, SIGIR.

[9]  Vern Paxson,et al.  @spam: the underground on 140 characters or less , 2010, CCS '10.

[10]  Dawn Xiaodong Song,et al.  Suspended accounts in retrospect: an analysis of twitter spam , 2011, IMC '11.

[11]  Ameet Talwalkar,et al.  MLlib: Machine Learning in Apache Spark , 2015, J. Mach. Learn. Res..

[12]  Alex Hai Wang,et al.  Don't follow me: Spam detection in Twitter , 2010, 2010 International Conference on Security and Cryptography (SECRYPT).

[13]  Filippo Menczer,et al.  BotOrNot: A System to Evaluate Social Bots , 2016, WWW.

[14]  Michael I. Jordan,et al.  Latent Dirichlet Allocation , 2001, J. Mach. Learn. Res..

[15]  Jacob Ratkiewicz,et al.  Detecting and Tracking Political Abuse in Social Media , 2011, ICWSM.

[16]  Alessandro Flammini,et al.  Predicting online extremism, content adopters, and interaction reciprocity , 2016, SocInfo.

[17]  Cheng Soon Ong,et al.  Multivariate spearman's ρ for aggregating ranks using copulas , 2016 .

[18]  Jure Leskovec,et al.  No country for old members: user lifecycle and linguistic change in online communities , 2013, WWW.

[19]  Leysia Palen,et al.  (How) will the revolution be retweeted?: information diffusion and the 2011 Egyptian uprising , 2012, CSCW.

[20]  Vern Paxson,et al.  Adapting Social Spam Infrastructure for Political Censorship , 2012, LEET.

[21]  Jong Kim,et al.  Spam Filtering in Twitter Using Sender-Receiver Relationship , 2011, RAID.

[22]  D. Boyd,et al.  The Arab Spring| The Revolutions Were Tweeted: Information Flows during the 2011 Tunisian and Egyptian Revolutions , 2011 .

[23]  Virgílio A. F. Almeida,et al.  Detecting Spammers on Twitter , 2010 .

[24]  Margaret E. Roberts,et al.  How the Chinese Government Fabricates Social Media Posts for Strategic Distraction, Not Engaged Argument , 2017, American Political Science Review.

[25]  Huan Liu,et al.  Social Spammer Detection in Microblogging , 2013, IJCAI.

[26]  Slava M. Katz,et al.  Estimation of probabilities from sparse data for the language model component of a speech recognizer , 1987, IEEE Trans. Acoust. Speech Signal Process..

[27]  Jacob Ratkiewicz,et al.  Political Polarization on Twitter , 2011, ICWSM.

[28]  Po-Ching Lin,et al.  A study of effective features for detecting long-surviving Twitter spam accounts , 2013, 2013 15th International Conference on Advanced Communications Technology (ICACT).

[29]  Gianluca Stringhini,et al.  Detecting spammers on social networks , 2010, ACSAC '10.