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Lessons for Extremist Content Moderation: Capturing a Visual Style of Extreme Right Imagery / CONNOR REES

Swansea University Author: CONNOR REES

DOI (Published version): 10.23889/SUThesis.69262

Abstract

Online platforms have been subject to a recent surge in legislation from governments across the globe that mandate the swift detection and removal of harmful content (including extremist content) from their platforms, often under tight deadlines and with substantial fines for non-compliance. This tr...

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Published: Swansea University, Wales, UK 2025
Institution: Swansea University
Degree level: Doctoral
Degree name: Ph.D
Supervisor: Nouri, L., and Müller, B.
URI: https://cronfa.swan.ac.uk/Record/cronfa69262
first_indexed 2025-04-10T12:41:50Z
last_indexed 2025-04-11T05:22:35Z
id cronfa69262
recordtype RisThesis
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spelling 2025-04-10T13:46:05.9305181 v2 69262 2025-04-10 Lessons for Extremist Content Moderation: Capturing a Visual Style of Extreme Right Imagery 8a46aa7bdafb0f9658139fbf9dadbee8 CONNOR REES CONNOR REES true false 2025-04-10 Online platforms have been subject to a recent surge in legislation from governments across the globe that mandate the swift detection and removal of harmful content (including extremist content) from their platforms, often under tight deadlines and with substantial fines for non-compliance. This trend, which opposes the historical self-regulation by online platforms, stems from several factors, not least being the apparent connection between online extremist rhetoric and real-world violence. Identifying and eliminating extremist content poses significant challenges, particularly concerning material from the extreme right. The extreme right frequently operates on the fringes of tolerable communication and utilises tactics like dog whistles (hiding secret messages within content such as imagery). This research focuses on extreme right imagery due to how challenging it can be to moderate. Research that analyses the extreme right has historically received less scholarly attention compared to other forms of extremism, namely Islamist extremism. The study involves an analysis of a dataset of designated extreme right group images taken from the encrypted messenger platform Telegram. This dataset is used in a mixed methods approach to iteratively design an extreme right image analysis framework. The purpose of this framework is to characterise the unique qualities and identifiers of extreme right content. The results and findings from creating the framework and using it to assess over 25000 images used to inform data-driven policy recommendations for online platforms to enhance their moderation practices. In doing so, this thesis aims to contribute new knowledge to the field and aid in moderation against the extreme right whilst protecting the freedom of expression of other online platform service users. E-Thesis Swansea University, Wales, UK Extreme Right, Online Extremism, Imagery 12 2 2025 2025-02-12 10.23889/SUThesis.69262 A selection of content is redacted or is partially redacted from this thesis to protect sensitive and personal information. COLLEGE NANME COLLEGE CODE Swansea University Nouri, L., and Müller, B. Doctoral Ph.D EPSRC, Meta EPSRC, Meta 2025-04-10T13:46:05.9305181 2025-04-10T13:33:58.6852137 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science CONNOR REES 1 69262__33996__2fa233bb7a9f43f29be48b828919f4bc.pdf 2025_Rees_C.final.69262.pdf 2025-04-10T13:40:49.0271587 Output 1791011 application/pdf E-Thesis – open access true Copyright: The Author, Conor Rees, 2025 true eng
title Lessons for Extremist Content Moderation: Capturing a Visual Style of Extreme Right Imagery
spellingShingle Lessons for Extremist Content Moderation: Capturing a Visual Style of Extreme Right Imagery
CONNOR REES
title_short Lessons for Extremist Content Moderation: Capturing a Visual Style of Extreme Right Imagery
title_full Lessons for Extremist Content Moderation: Capturing a Visual Style of Extreme Right Imagery
title_fullStr Lessons for Extremist Content Moderation: Capturing a Visual Style of Extreme Right Imagery
title_full_unstemmed Lessons for Extremist Content Moderation: Capturing a Visual Style of Extreme Right Imagery
title_sort Lessons for Extremist Content Moderation: Capturing a Visual Style of Extreme Right Imagery
author_id_str_mv 8a46aa7bdafb0f9658139fbf9dadbee8
author_id_fullname_str_mv 8a46aa7bdafb0f9658139fbf9dadbee8_***_CONNOR REES
author CONNOR REES
author2 CONNOR REES
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publishDate 2025
institution Swansea University
doi_str_mv 10.23889/SUThesis.69262
college_str Faculty of Science and Engineering
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hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Mathematics and Computer Science - Computer Science{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Mathematics and Computer Science - Computer Science
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description Online platforms have been subject to a recent surge in legislation from governments across the globe that mandate the swift detection and removal of harmful content (including extremist content) from their platforms, often under tight deadlines and with substantial fines for non-compliance. This trend, which opposes the historical self-regulation by online platforms, stems from several factors, not least being the apparent connection between online extremist rhetoric and real-world violence. Identifying and eliminating extremist content poses significant challenges, particularly concerning material from the extreme right. The extreme right frequently operates on the fringes of tolerable communication and utilises tactics like dog whistles (hiding secret messages within content such as imagery). This research focuses on extreme right imagery due to how challenging it can be to moderate. Research that analyses the extreme right has historically received less scholarly attention compared to other forms of extremism, namely Islamist extremism. The study involves an analysis of a dataset of designated extreme right group images taken from the encrypted messenger platform Telegram. This dataset is used in a mixed methods approach to iteratively design an extreme right image analysis framework. The purpose of this framework is to characterise the unique qualities and identifiers of extreme right content. The results and findings from creating the framework and using it to assess over 25000 images used to inform data-driven policy recommendations for online platforms to enhance their moderation practices. In doing so, this thesis aims to contribute new knowledge to the field and aid in moderation against the extreme right whilst protecting the freedom of expression of other online platform service users.
published_date 2025-02-12T05:35:14Z
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score 11.110217

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