Detecting weak and strong Islamophobic hate speech on social media

By Bertie Vidgen, Taha Yasseri. 2018

161 citations recorded by the source corpus.

ABSTRACT Islamophobic hate speech on social media is a growing concern in contemporary Western politics and society. It can inflict considerable harm on any victims who are targeted, create a sense of fear and exclusion amongst their communities, toxify public discourse and motivate other forms of extremist and hateful behavior. Accordingly, there is a pressing need for automated tools to detect and classify Islamophobic hate speech robustly and at scale, thereby enabling quantitative analyses of large textual datasets, such as those collected from social media. Previous research has mostly approached the automated detection of hate speech as a binary task. However, the varied nature of Islamophobia means that this is often inappropriate for both theoretically informed social science and effective monitoring of social media platforms. Drawing on in-depth conceptual work we build an automated software tool which distinguishes between non-Islamophobic, weak Islamophobic and strong Islamophobic content. Accuracy is 77.6% and balanced accuracy is 83%. Our tool enables future quantitative research into the drivers, spread, prevalence and effects of Islamophobic hate speech on social media.

Subfields: Artificial Intelligence, Communication, Sociology and Political Science, AI Ethics, Fairness & Society

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Bertie Vidgen
2. Valerio Basile
3. Viviana Patti
4. Helen Z. Margetts
5. Dong Nguyen
6. Manuela Sanguinetti
7. Fabio Poletto
8. Patrícia G. C. Rossini

In Communication

1. Valerio Basile
2. Dong Nguyen
3. Viviana Patti
4. Helen Z. Margetts
5. Bertie Vidgen

In Sociology and Political Science

1. Bertie Vidgen
2. Helen Z. Margetts
3. Dong Nguyen
4. Viviana Patti
5. Valerio Basile

In AI Ethics, Fairness & Society

1. Dong Nguyen
2. Viviana Patti
3. Helen Z. Margetts
4. Manuela Sanguinetti
5. Bertie Vidgen
6. Valerio Basile
7. Cristina Bosco
8. Rebekah Tromble

Citations in the corpus

• Resources and benchmark corpora for hate speech detection: a systematic review (2020, background): “Vidgen and Yasseri (2020) (VY)—4000 tweets in English, annotated by experts as ‘‘ not islamophobic/weakly islamophobic/strongly islamophobic ’’.”
• Introducing CAD: the Contextual Abuse Dataset (2021, background): “…al., 2020), others cover only abuse against identities (Davidson et al., 2017; Fortuna and Nunes, 2018; Kiela et al., 2020), against only a single identity, such as misogyny (Anzovino et al., 2018) or Islamophobia (Vidgen and Yasseri, 2019), or only abuse against individuals (Wulczyn et al., 2017).”