The DOI matches its Crossref record; 45 of the 47 references it cites were found in a registry or an index.
Hate Lingo: A Target-Based Linguistic Analysis of Hate Speech in Social Media
Open accessAbstract
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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]
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
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.
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
Developing an online hate classifier for multiple social media platforms
Salminen, J. O., Hopf, M., Chowdhury, S. A., Jung, S., Almerekhi, H., & Jansen, B. J. J. (2020) · Human-centric Computing and Information Sciences · doi:10.1186/s13673-019-0205-6 · Cited by 317
Misinformation, Disinformation, and Online Propaganda
Guess, A. M., & Lyons, B. A. (2020) · Social Media and Democracy · doi:10.1017/9781108890960.003 · Cited by 284
Vulnerable community identification using hate speech detection on social media
Mossie, Z., & Wang, J. (2019) · Information Processing & Management · doi:10.1016/j.ipm.2019.102087 · Cited by 189
A Benchmark Dataset for Learning to Intervene in Online Hate Speech
Qian, J., Bethke, A., Liu, Y., Belding, E., & Wang, W. Y. (2019) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d19-1482 · Cited by 190
Self-citation
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 ›
Are you an author, or do you see a problem with this page? Request a review
