Predicting Online Islamophobic Behavior after #ParisAttack

Kareem Darwish, Walid Magdy, A. Rahimi, N. Abukhodair, T. Baldwin

Research output: Contribution to journalArticlepeer-review


The Paris terrorist attacks occurred on November 13, 2015, prompting a massive response on social media including Twitter, with millions of posted tweets in the first few hours after the attacks. Most of the tweets were condemning the attacks and showing support to Parisians. One of the trending debates related to the attacks concerned possible association between terrorism and Islam, and Muslims in general. This created a global discussion between those attacking and those defending Islam and Muslims. In this paper, we use this incident to examine the effect of online social network interactions prior to an event to predict what attitudes will be expressed in response to the event. Specifically, we focus on how a person's online content and network dynamics can be used to predict future attitudes and stance in the aftermath of a major event.
In our study, we collected a set of 8.36 million tweets related to the Paris attacks within the 50 hours following the event, of which we identified over 900k tweets mentioning Islam and Muslims. We quantitatively analyzed users' network interactions and historical tweets to predict their attitudes towards Islam and Muslim.
We provide a description of the quantitative results based on the content (hashtags) and network interactions (retweets, replies, and mentions). We analyze two types of data: (1) we use post-event tweets to learn users' stated stance towards Muslims based on sampling methods and crowd-sourced annotations; and (2) we employ pre-event interactions on Twitter to build a classifier to predict post-event stance. We found that pre-event network interactions can predict attitudes towards Muslims with 82\% macro F-measure, even in the absence of prior mentions of Islam, Muslims, or related terms.
Original languageEnglish
Number of pages18
JournalJournal of Web Science
Issue number1
Early online date15 Oct 2017
Publication statusE-pub ahead of print - 15 Oct 2017


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