Network, Text, and Image Analysis of Anti-Muslim Groups on Facebook




Islamophobic attitudes and overt acts of hostility toward Muslims in the United States are increasingly commonplace. The goal of this research is to begin to understand how anti-Muslim political groups use the Facebook social network to build their online communities and perpetuate their beliefs. We used the public Facebook Graph API to create a large dataset of 700,204 members of 1,870 Facebook groups spanning 10 different far-right ideologies during the time period June 2017 - March 2018. We first applied social network analysis techniques to discover which groups and ideologies shared members with anti-Muslim groups during this period. Our results show that the anti-Muslim Facebook network has unique characteristics when compared to other categories of far-right extremism. We then assessed 202 anti-Muslim Facebook group cover photos and descriptions for evidence of Islamophobic content. Results indicate that these anti-Muslim groups rely on a predictable collection of visual and linguistic cues to propagate negative stereotypes about Muslims, and that the vast majority of these groups rely heavily on symbols and language that portray Islam as a violent enemy which is deserving of violence and hostility in return. By understanding the important role Islamophobia plays in the hate ecosystem on Facebook, social media users and platform providers can be better prepared to confront and condemn anti-Muslim bias.

Author Biography

  • Megan Squire, Elon University
    Professor of Computer Science


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