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#FailedRevolutions: Using Twitter to Study the Antecedents of ISIS Support

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arxiv 1503.02401 v1 pith:TOQJOMIE submitted 2015-03-09 cs.SI physics.soc-ph

classification cs.SIphysics.soc-ph
keywords isissupportoppositionstateantecedentsarabdatafailed
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Within a fairly short amount of time, the Islamic State of Iraq and Syria (ISIS) has managed to put large swaths of land in Syria and Iraq under their control. To many observers, the sheer speed at which this "state" was established was dumbfounding. To better understand the roots of this organization and its supporters we present a study using data from Twitter. We start by collecting large amounts of Arabic tweets referring to ISIS and classify them into pro-ISIS and anti-ISIS. This classification turns out to be easily done simply using the name variants used to refer to the organization: the full name and the description as "state" is associated with support, whereas abbreviations usually indicate opposition. We then "go back in time" by analyzing the historic timelines of both users supporting and opposing and look at their pre-ISIS period to gain insights into the antecedents of support. To achieve this, we build a classifier using pre-ISIS data to "predict", in retrospect, who will support or oppose the group. The key story that emerges is one of frustration with failed Arab Spring revolutions. ISIS supporters largely differ from ISIS opposition in that they refer a lot more to Arab Spring uprisings that failed. We also find temporal patterns in the support and opposition which seems to be linked to major news, such as reported territorial gains, reports on gruesome acts of violence, and reports on airstrikes and foreign intervention.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Social Influence and Radicalization: A Social Data Analytics Study

    cs.CY 2019-09 reject novelty 4.0 of 10

    iRadical combines community detection and particle swarm optimization for influence maximization with keyword counting over radicalization criteria to score Twitter users, but the evaluation is limited and partly circular.

  2. Deception Strategies and Threats for Online Discussions

    cs.SI 2019-06 unverdicted

    A review of historical deception strategies in communication and their application to current online threats from social bots and disinformation.

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