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Title: Tweeting Islamophobia
Author: Vidgen, Bertram
ISNI:       0000 0004 7971 6545
Awarding Body: University of Oxford
Current Institution: University of Oxford
Date of Award: 2019
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The great promise of social media platforms such as Twitter is to connect people separated across time and space. This has had far-ranging consequences for politics by changing discursive, participative and organisational practices. However, despite much early techno-optimism about platforms like Twitter, concerns are growing that they enable harmful, hateful and divisive behaviours. In this thesis, I focus on one of the most concerning and harmful behaviours on Twitter and in politics more broadly: Islamophobic hate speech. The socio-political consequences of hate speech are deeply concerning, and include causing harm to targeted victims, spreading divisiveness, and normalizing dangerous and extremist ideas. The aim of this thesis is to enhance our understanding of the nature and dynamics of Islamophobic hate speech amongst followers of UK political parties on Twitter. I study four parties from across the political spectrum: the BNP, UKIP, the Conservatives and Labour. I make three main contributions. First, I define Islamophobia in terms of negativity and generality, thus making a robust, theoretically-informed contribution to the study of a deeply contested concept. This argument informs the second contribution, which is methodological: I create a multi-class supervised machine learning classifier for Islamophobic hate speech. This distinguishes between weak and strong varieties and can be applied robustly and at scale. My third contribution is theoretical. Drawing together my substantive findings, I argue that Islamophobic tweeting amongst followers of UK parties can be characterised as a wind system which contains Islamophobic hurricanes. This analogy captures the complex, heterogeneous dynamics underpinning Islamophobia on Twitter, and highlights its devastating effects. I also show that Islamist terrorist attacks drive Islamophobia, and that this affects followers of all four parties studied here. I use this finding to extend the theory of cumulative extremism beyond extremist groups to include individuals with mainstream affiliations. These contributions feed into ongoing academic, policymaking and activist discussions about Islamophobic hate speech in both social media and UK politics.
Supervisor: Yasseri, Taha ; Margetts, Helen Sponsor: Economic and Social Research Council ; Alan Turing Institute
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: online politics ; internet studies ; social science ; political science