Online Hate Detection Systems: Challenges and Action Points for Developers, Data Scientists, and Researchers

Joni Salminen, Maria Jose Linarez, Soon Gyo Jung, Bernard J. Jansen

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Citations (Scopus)

Abstract

Automated online hate detection has garnered interest from various stakeholders to make online platforms safer. Despite this interest, there remain a plethora of unresolved issues that hinder advancement. We review fourteen state-of-the-art articles discussing these challenges, and present a meta-synthesis. Six themes are identified: (1) Dataset selection, (2) Detection of False Positives and Negatives, (3) Semantic Context of Hate Messages, (4) Privacy and Anonymity, (5) Ethical Considerations, and (6) Minimizing Bias. For each theme, we provide a set of action points to support researchers, data scientists, and developers to improve hate detection systems.

Original languageEnglish
Title of host publicationProceedings of 2021 8th IEEE International Conference on Behavioural and Social Computing, BESC 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665400237
DOIs
Publication statusPublished - 2021
Event8th IEEE International Conference on Behavioural and Social Computing, BESC 2021 - Virtual, Doha, Qatar
Duration: 29 Oct 202131 Oct 2021

Publication series

NameProceedings of 2021 8th IEEE International Conference on Behavioural and Social Computing, BESC 2021

Conference

Conference8th IEEE International Conference on Behavioural and Social Computing, BESC 2021
Country/TerritoryQatar
CityVirtual, Doha
Period29/10/2131/10/21

Keywords

  • Hate detection systems
  • Online hate
  • Social media

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