IDEAS home Printed from https://ideas.repec.org/a/sae/sagope/v9y2019i1p2158244018789229.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/2158244018789229
    Download Restriction: no

    File URL: https://libkey.io/10.1177/2158244018789229?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Rauh, Christian, 2018. "Validating a sentiment dictionary for German political language—a workbench note," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 15(4), pages 319-343.
    2. James Evans, 2022. "From Text Signals to Simulations: A Review and Complement to Text as Data by Grimmer, Roberts & Stewart (PUP 2022)," Sociological Methods & Research, , vol. 51(4), pages 1868-1885, November.
    3. Valentino Larcinese & Luke Miner, 2018. "Was Obama Elected by the Internet? Broadband Diffusion and Voters' Behavior in US Presidential Elections," CESifo Working Paper Series 6882, CESifo.
    4. Angela Chang & Peter J. Schulz & Angus Wenghin Cheong, 2020. "Online Newspaper Framing of Non-Communicable Diseases: Comparison of Mainland China, Taiwan, Hong Kong and Macao," IJERPH, MDPI, vol. 17(15), pages 1-15, August.
    5. Margaret E. Roberts & Brandon M. Stewart & Richard A. Nielsen, 2020. "Adjusting for Confounding with Text Matching," American Journal of Political Science, John Wiley & Sons, vol. 64(4), pages 887-903, October.
    6. Rebecca Cordell & Kristian Skrede Gleditsch & Florian G Kern & Laura Saavedra-Lux, 2020. "Measuring institutional variation across American Indian constitutions using automated content analysis," Journal of Peace Research, Peace Research Institute Oslo, vol. 57(6), pages 777-788, November.
    7. Lehotský, Lukáš & Černoch, Filip & Osička, Jan & Ocelík, Petr, 2019. "When climate change is missing: Media discourse on coal mining in the Czech Republic," Energy Policy, Elsevier, vol. 129(C), pages 774-786.
    8. Weifeng Zhong, 2016. "The candidates in their own words: A textual analysis of 2016 president primary debates," AEI Economic Perspectives, American Enterprise Institute, April.
    9. Brownback, Andy & Novotny, Aaron, 2018. "Social desirability bias and polling errors in the 2016 presidential election," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 74(C), pages 38-56.
    10. Sergei Guriev & Daniel Treisman, 2019. "Informational Autocrats," Journal of Economic Perspectives, American Economic Association, vol. 33(4), pages 100-127, Fall.
    11. Laura K. Nelson & Derek Burk & Marcel Knudsen & Leslie McCall, 2021. "The Future of Coding: A Comparison of Hand-Coding and Three Types of Computer-Assisted Text Analysis Methods," Sociological Methods & Research, , vol. 50(1), pages 202-237, February.
    12. Lundberg, Ian & Brand, Jennie E. & Jeon, Nanum, 2022. "Researcher reasoning meets computational capacity: Machine learning for social science," SocArXiv s5zc8, Center for Open Science.
    13. Jae Yeon Kim, 2021. "Integrating human and machine coding to measure political issues in ethnic newspaper articles," Journal of Computational Social Science, Springer, vol. 4(2), pages 585-612, November.
    14. Rodrigo Zamith & Seth C. Lewis, 2015. "Content Analysis and the Algorithmic Coder," The ANNALS of the American Academy of Political and Social Science, , vol. 659(1), pages 307-318, May.
    15. Gavin Abercrombie & Riza Batista-Navarro, 2020. "Sentiment and position-taking analysis of parliamentary debates: a systematic literature review," Journal of Computational Social Science, Springer, vol. 3(1), pages 245-270, April.
    16. Michelle L. Johnson & Lindsay K. Campbell & Erika S. Svendsen & Heather L. McMillen, 2019. "Mapping Urban Park Cultural Ecosystem Services: A Comparison of Twitter and Semi-Structured Interview Methods," Sustainability, MDPI, vol. 11(21), pages 1-21, November.
    17. Jetter, Michael, 2017. "The effect of media attention on terrorism," Journal of Public Economics, Elsevier, vol. 153(C), pages 32-48.
    18. Giovanni Di Franco & Michele Santurro, 2021. "Machine learning, artificial neural networks and social research," Quality & Quantity: International Journal of Methodology, Springer, vol. 55(3), pages 1007-1025, June.
    19. Zhang, Han, 2021. "How Using Machine Learning Classification as a Variable in Regression Leads to Attenuation Bias and What to Do About It," SocArXiv 453jk, Center for Open Science.
    20. Martin Haselmayer & Marcelo Jenny, 2017. "Sentiment analysis of political communication: combining a dictionary approach with crowdcoding," Quality & Quantity: International Journal of Methodology, Springer, vol. 51(6), pages 2623-2646, November.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:sae:sagope:v:9:y:2019:i:1:p:2158244018789229. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: SAGE Publications (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.