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ISIS at Its Apogee: The Arabic Discourse on Twitter and What We Can Learn From That About ISIS Support and Foreign Fighters

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  • Andrea Ceron
  • Luigi Curini
  • Stefano M. Iacus

Abstract

We analyze 26.2 million comments published in Arabic language on Twitter, from July 2014 to January 2015, when Islamic State of Iraq and Syria (ISIS)’s strength reached its peak and the group was prominently expanding the territorial area under its control. By doing that, we are able to measure the share of support and aversion toward the Islamic State within the online Arab communities. We then investigate two specific topics. First, by exploiting the time granularity of the tweets, we link the opinions with daily events to understand the main determinants of the changing trend in support toward ISIS. Second, by taking advantage of the geographical locations of tweets, we explore the relationship between online opinions across countries and the number of foreign fighters joining ISIS.

Suggested Citation

  • Andrea Ceron & Luigi Curini & Stefano M. Iacus, 2019. "ISIS at Its Apogee: The Arabic Discourse on Twitter and What We Can Learn From That About ISIS Support and Foreign Fighters," SAGE Open, , vol. 9(1), pages 21582440187, March.
    • Handle: RePEc:sae:sagope:v:9:y:2019:i:1:p:2158244018789229
    DOI: 10.1177/2158244018789229
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    References listed on IDEAS

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    1. Grimmer, Justin & Stewart, Brandon M., 2013. "Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts," Political Analysis, Cambridge University Press, vol. 21(3), pages 267-297, July.
    2. Stephens-Davidowitz, Seth, 2014. "The cost of racial animus on a black candidate: Evidence using Google search data," Journal of Public Economics, Elsevier, vol. 118(C), pages 26-40.
    3. Fisher, Robert J, 1993. "Social Desirability Bias and the Validity of Indirect Questioning," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 20(2), pages 303-315, September.
    4. Bruno S. Frey & Simon Luechinger, "undated". "How to Fight Terrorism: Alternatives to Deterrence," IEW - Working Papers 137, Institute for Empirical Research in Economics - University of Zurich.
    5. Adam Badawy & Emilio Ferrara, 2018. "The rise of Jihadist propaganda on social networks," Journal of Computational Social Science, Springer, vol. 1(2), pages 453-470, September.
    6. Daniel J. Hopkins & Gary King, 2010. "A Method of Automated Nonparametric Content Analysis for Social Science," American Journal of Political Science, John Wiley & Sons, vol. 54(1), pages 229-247, January.
    7. King, Gary & Pan, Jennifer & Roberts, Margaret E., 2013. "How Censorship in China Allows Government Criticism but Silences Collective Expression," American Political Science Review, Cambridge University Press, vol. 107(2), pages 326-343, May.
    8. Nader Hashemi & Danny Postel, 2017. "Sectarianization: Mapping the New Politics of the Middle East," The Review of Faith & International Affairs, Taylor & Francis Journals, vol. 15(3), pages 1-13, July.
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