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Badly Evolved? Exploring Long-Surviving Suspicious Users on Twitter

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Social Informatics (SocInfo 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10539))

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Abstract

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.

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Notes

  1. 1.

    We consider an account to be active and long-surviving if it had tweeted at least once on at least six different months in 2015.

  2. 2.

    The website hosting this dataset has been taken offline but we were able to recover accounts from http://archive.is/A6f3L.

  3. 3.

    Contact the first author for access to the ISIS dataset.

  4. 4.

    https://support.twitter.com/articles/18311-the-twitter-rules.

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Acknowledgments

This work was supported in part by AFOSR grant FA9550-15-1-0149. Majid Alfifi is partially funded by a scholarship from King Fahd University of Petroleum and Minerals. Any opinions, findings and conclusions or recommendations expressed in this material are the author(s) and do not necessarily reflect those of the sponsors. We’d like to also thank the anonymous reviewers for their helpful feedback.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Texas A&M University, College Station, TX, USA

    Majid Alfifi & James Caverlee

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  1. Majid Alfifi
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  2. James Caverlee
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Correspondence to Majid Alfifi .

Editor information

Editors and Affiliations

  1. Indiana University, Bloomington, Indiana, USA

    Giovanni Luca Ciampaglia

  2. University of Washington, Seattle, Washington, USA

    Afra Mashhadi

  3. University of Oxford, Oxford, United Kingdom

    Taha Yasseri

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Alfifi, M., Caverlee, J. (2017). Badly Evolved? Exploring Long-Surviving Suspicious Users on Twitter. In: Ciampaglia, G., Mashhadi, A., Yasseri, T. (eds) Social Informatics. SocInfo 2017. Lecture Notes in Computer Science(), vol 10539. Springer, Cham. https://doi.org/10.1007/978-3-319-67217-5_14

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  • DOI: https://doi.org/10.1007/978-3-319-67217-5_14

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