Detecting the Hate Code on Social Media
Open accessAbstract
Social media has become an indispensable part of the everyday lives of millions of people around the world. It provides a platform for expressing opinions and beliefs, communicated to a massive audience. However, this ease with which people can express themselves has also allowed for the large scale spread of propaganda and hate speech. To prevent violating the abuse policies of social media platforms and also to avoid detection by automatic systems like Google’s Conversation AI, racists have begun to use a code (a movement termed Operation Google). This involves substituting references to communities by benign words that seem out of context, in hate filled posts or Tweets. For example, users have used the words Googles and Bings to represent the African-American and Asian communities, respectively. By generating the list of users who post such content, we move a step forward from classifying tweets by allowing us to study the usage pattern of these concentrated set of users.
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Online abusers have adapted, learning to replace racist words with other more benign terms and phrases to avoid detection.
“In addition, online abusers have also adapted, learning to replace racist words with other more benign terms and phrases to avoid detection(Magu etal. 2017).”
From Misogynoir: challenges in detecting intersectional hate (2022) · cites this paper as Magu et al. 2017
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Research works printed but not linked: 7
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Cited by
Sorted by citations
Social Media, Echo Chambers, and Political Polarization
Barberá, P. (2020) · Social Media and Democracy · doi:10.1017/9781108890960.004 · Cited by 373 · see citing works
Misogynoir: challenges in detecting intersectional hate
Kwarteng, J., Perfumi, S. C., Farrell, T., Third, A., & Fernandez, M. (2022) · Social Network Analysis and Mining · doi:10.1007/s13278-022-00993-7 · Cited by 30 · see citing works
“In addition, online abusers have also adapted, learning to replace racist words with other more benign terms and phrases to avoid detection (Magu et al. 2017).”
BackgroundLearning Invariant Representations of Social Media Users
Andrews, N., & Bishop, M. (2019) · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) · doi:10.18653/v1/d19-1178 · Cited by 19 · see citing works
- Authors
- Magu, R., Joshi, K., & Luo, J.
- Affiliations
- University of Rochester (US)
- Venue
- Proceedings of the International AAAI Conference on Web and Social Media
- Published
- 2017-05-03
- Volume
- 11
- Issue
- 1
- Pages
- 608-611
- ISSN
- 2162-3449 (ISSN-L)
- 2334-0770
- OpenAlex
- W2963992732
- Cited by
- 93
- References
- 0
- Citations by year
- 2026: 4 · 2025: 6 · 2024: 5 · 2023: 12 · 2022: 10 · 2021: 13 · 2020: 25 · 2019: 5 · 2018: 9 · 2017: 4
- FWCI
- 5.36
- Type
- conference paper
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- Proceedings of the International AAAI Conference on Web and Social Media (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- AAAI Publications (The Association for the Advancement of Artificial Intelligence (AAAI)) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Hate Speech and Cyberbullying Detection
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Physical Sciences › Computer Science › Information Systems › Spam and Phishing Detection
- Keywords
- hate speech detection, social media, Twitter, user profiling
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