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Hate Lingo: A Target-Based Linguistic Analysis of Hate Speech in Social Media
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While social media empowers freedom of expression and individual voices, it also enables anti-social behavior, online harassment, cyberbullying, and hate speech. In this paper, we deepen our understanding of online hate speech by focusing on a largely neglected but crucial aspect of hate speech -- its target: either directed towards a specific person or entity, or generalized towards a group of people sharing a common protected characteristic. We perform the first linguistic and psycholinguistic analysis of these two forms of hate speech and reveal the presence of interesting markers that distinguish these types of hate speech. Our analysis reveals that Directed hate speech, in addition to being more personal and directed, is more informal, angrier, and often explicitly attacks the target (via name calling) with fewer analytic words and more words suggesting authority and influence. Generalized hate speech, on the other hand, is dominated by religious hate, is characterized by the use of lethal words such as murder, exterminate, and kill; and quantity words such as million and many. Altogether, our work provides a data-driven analysis of the nuances of online-hate speech that enables not only a deepened understanding of hate speech and its social implications, but also its detection.
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“For example, the dataset used to train HateSonar was built using a Hatebase 13 hate speech vocabulary, including commonly used terms and phrases on the internet, which is likely to create non-representative training data with other nuances of hate uncounted for.”
From Misogynoir: challenges in detecting intersectional hate (2022) · cites this paper as [13]
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Baker, C. F., Fillmore, C. J., & Lowe, J. B. (1998) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.3115/980845.980860 · Cited by 2,644
Automated Hate Speech Detection and the Problem of Offensive Language
Davidson, T., Warmsley, D., Macy, M., & Weber, I. (2017) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v11i1.14955 · Cited by 2,610
Hateful Symbols or Hateful People? Predictive Features for Hate Speech Detection on Twitter
Talat, Z., & Hovy, D. (2016) · North American Chapter of the Association for Computational Linguistics · doi:10.18653/v1/n16-2013 · Cited by 1,919
A Survey on Hate Speech Detection using Natural Language Processing
Schmidt, A., & Wiegand, M. (2017) · SocialNLP@EACL · doi:10.18653/v1/w17-1101 · Cited by 1,478
Abusive Language Detection in Online User Content
Nobata, C., Tetreault, J., Thomas, A. O., Mehdad, Y., & Chang, Y. (2016) · International World Wide Web Conference (WWW) · doi:10.1145/2872427.2883062 · Cited by 1,197
Named Entity Recognition in Tweets: An Experimental Study
Ritter, A., Clark, S., & Etzioni, O. (2011) · Cited by 1,182
FrameNet II: Extended theory and practice
Ruppenhofer, J., Ellsworth, M., Petruck, M. R. L., Johnson, C. R., & Scheffczyk, J. (2006) · Publication Server of the Institute for German Language (Institute for German Language) · Cited by 1,065
Is the Sample Good Enough? Comparing Data from Twitter's Streaming API with Twitter's Firehose
Morstatter, F., Pfeffer, J., Liu, H., & Carley, K. (2021) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v7i1.14401 · Cited by 863
Tufekci, Z. (2014) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v8i1.14517 · Cited by 780
Hate Speech Detection with Comment Embeddings
Djuric, N., Zhou, J. L., Morris, R., Grbovic, M., Radosavljević, V., & Bhamidipati, N. L. (2015) · International World Wide Web Conference (WWW) · doi:10.1145/2740908.2742760 · Cited by 725
Research works printed but not linked: 1
- [4]4. Chen, D.; Schneider, N.; Das, D.; and Smith, N. A. 2010. Se- mafor: Frame Argument Resolution with Log-linear Mod- els. In Proceedings of the 5th International Workshop on Semantic Evaluation.
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Metadata
- Authors
- ElSherief, M., Kulkarni, V., Nguyen, D., Yang Wang, W., & Belding, E.
- Affiliations
- University of California, Santa Barbara (US)
- Venue
- Proceedings of the International AAAI Conference on Web and Social Media
- Published
- 2018-06-15
- Volume
- 12
- Issue
- 1
- ISSN
- 2162-3449 (ISSN-L)
- 2334-0770
- OpenAlex
- W2797630160
- Cited by
- 294
- References
- 47
- Citations by year
- 2026: 16 · 2025: 34 · 2024: 25 · 2023: 44 · 2022: 37 · 2021: 43 · 2020: 54 · 2019: 34 · 2018: 6
- FWCI
- 13.93
- Type
- conference paper
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- Proceedings of the International AAAI Conference on Web and Social Media (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Hate Speech and Cyberbullying Detection
- Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
- Social Sciences › Psychology › Social Psychology › Mental Health via Writing
- Keywords
- online hate speech, social media, linguistic markers, psycholinguistic analysis, cyberbullying, online harassment, name-calling, hate speech detection
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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