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Detecting Users Who Share Extremist Content on Twitter

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Surveillance in Action

Abstract

Identifying extremist-associated conversations on social media sites and blog forums is still an open problem. Extremist groups leverage social media to (1) spread their message and (2) gain recruits. In this chapter, we look at different work in this arena, focusing on metrics and features that researchers have proposed as proxies for misbehavior on Twitter. We begin this chapter by analyzing potential features a small amount of manually labeled data about ISIS supporters on Twitter. We then group these features into categories related to tweet content, viewpoints, and dynamics. After discussing different state of the art methods for extremism detection and similar problems, we present a case study looking at the ISIS extremist group. Finally, we discuss how one collects these data for a surveillance system and conclude by discussing some current challenges and future directions for effective surveillance of extremism.

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Acknowledgements

We would like to acknowledge the Georgetown Institute for the Study of International Migration (ISIM) for their subject matter expertise throughout the process. This work was supported in part by the National Science Foundation (NSF) Grant SMA-1338507 and the Georgetown University Mass Data Institute (MDI). Any opinions, findings, conclusions, and recommendations expressed in this work are those of the authors and do not necessarily reflect the views of NSF or MDI.

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Authors and Affiliations

  1. Georgetown University, Washington, DC, USA

    Yifang Wei & Lisa Singh

Authors
  1. Yifang Wei
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  2. Lisa Singh
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Corresponding author

Correspondence to Yifang Wei .

Editor information

Editors and Affiliations

  1. Department of Informatics and Computers, Hellenic Air Force Academy, Attica, Greece

    Panagiotis Karampelas

  2. Multispectral Imagery Lab, Lane Department of Computer Science and Electrical Engineering, West Virginia University, Morgantown, West Virginia, USA

    Thirimachos Bourlai

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Wei, Y., Singh, L. (2018). Detecting Users Who Share Extremist Content on Twitter. In: Karampelas, P., Bourlai, T. (eds) Surveillance in Action. Advanced Sciences and Technologies for Security Applications. Springer, Cham. https://doi.org/10.1007/978-3-319-68533-5_17

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