Detecting weak and strong Islamophobic hate speech on social media

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Title: Detecting weak and strong Islamophobic hate speech on social media
Authors: Vidgen, BertieYasseri, Taha
Permanent link: http://hdl.handle.net/10197/12720
Date: 2020
Online since: 2022-01-12T12:54:35Z
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.
Funding Details: Engineering and Physical Sciences Research Council
Type of material: Journal Article
Publisher: Taylor & Francis
Journal: Journal of Information Technology and Politics
Volume: 17
Issue: 1
Start page: 66
End page: 78
Copyright (published version): 2019 Taylor & Francis
Keywords: CommunicationHate speechIslamophobiaPrejudiceSocial mediaNatural language processingMachine learningBig dataTwitterScienceSupportScale
DOI: 10.1080/19331681.2019.1702607
Language: en
Status of Item: Peer reviewed
ISSN: 1933-1681
This item is made available under a Creative Commons License: https://creativecommons.org/licenses/by-nc-nd/3.0/ie/
Appears in Collections:Sociology Research Collection
Geary Institute Research Collection

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