The DOI matches its Crossref record; 37 of the 37 references it cites were found in a registry or an index.
Hate speech detection: A solved problem? The challenging case of long tail on Twitter
Open accessAbstract
In recent years, the increasing propagation of hate speech on social media and the urgent need for effective counter-measures have drawn significant investment from governments, companies, and researchers. A large number of methods have been developed for automated hate speech detection online. Thi s aims to classify textual content into non-hate or hate speech, in which case the method may also identify the targeting characteristics (i.e., types of hate, such as race, and religion) in the hate speech. However, we notice significant difference between the performance of the two (i.e., non-hate vs. hate). In this work, we argue for a focus on the latter problem for practical reasons. We show that it is a much more challenging task, as our analysis of the language in the typical datasets shows that hate speech lacks unique, discriminative features and therefore is found in the ‘long tail’ in a dataset that is difficult to discover. We then propose Deep Neural Network structures serving as feature extractors that are particularly effective for capturing the semantics of hate speech. Our methods are evaluated on the largest collection of hate speech datasets based on Twitter, and are shown to be able to outperform the best performing method by up to 5 percentage points in macro-average F1, or 8 percentage points in the more challenging case of identifying hateful content.
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.
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
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
Glove: Global Vectors for Word Representation
Pennington, J., Socher, R., & Manning, C. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1162 · Cited by 34,071
Efficient Estimation of Word Representations in Vector Space
Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013) · arXiv · doi:10.48550/arxiv.1301.3781 · Cited by 26,568
Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., & Yuille, A. L. (2016) · arXiv · doi:10.48550/arxiv.1606.00915 · Cited by 22,392
Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., & Yuille, A. L. (2018) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2017.2699184 · Cited by 22,392
Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling
Chung, J., Gulcehre, C., Cho, K., & Bengio, Y. (2014) · arXiv · doi:10.48550/arxiv.1412.3555 · Cited by 11,008
Deep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition
Ordóñez, F., & Roggen, D. (2016) · Sensors · doi:10.3390/s16010115 · Cited by 2,761
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
Waseem, Z., & Hovy, D. (2016) · Proceedings of the NAACL Student Research Workshop · doi:10.18653/v1/n16-2013 · Cited by 1,655
A Survey on Hate Speech Detection using Natural Language Processing
Schmidt, A., & Wiegand, M. (2017) · Proceedings of the Fifth International Workshop on Natural Language Processing for Social Media · doi:10.18653/v1/w17-1101 · Cited by 1,395
Research works printed but not linked: 1
- Nockleby, 2000, Hate Speech, 1277
Cited by
Internet, social media and online hate speech. Systematic review
Castaño-Pulgarín, S. A., Suárez-Betancur, N., Vega, L. M. T., & López, H. M. H. (2021) · Aggression and Violent Behavior · doi:10.1016/j.avb.2021.101608 · Cited by 395
Gongane, V. U., Munot, M. V., & Anuse, A. D. (2022) · Social Network Analysis and Mining · doi:10.1007/s13278-022-00951-3 · Cited by 153
Combining FastText and Glove Word Embedding for Offensive and Hate speech Text Detection
Badri, N., Kboubi, F., & Chaibi, A. H. (2022) · Procedia Computer Science · doi:10.1016/j.procs.2022.09.132 · Cited by 65
Ahmed, T., Ivan, S., Kabir, M., Mahmud, H., & Hasan, K. (2022) · Social Network Analysis and Mining · doi:10.1007/s13278-022-00934-4 · Cited by 42
Buan, T. A., & Ramachandra, R. (2020) · Proceedings of the 2020 4th International Conference on Compute and Data Analysis · doi:10.1145/3388142.3388147 · Cited by 14
Metadata
- Authors
- Zhang, Z., & Luo, L.
- Venue
- Semantic Web
- Publisher
- SAGE Publications
- Published
- 2019-09-26
- Volume
- 10
- Issue
- 5
- Pages
- 925-945
- ISSN
- 1570-0844 (ISSN-L)
- 2210-4968
- OpenAlex
- W2963790884
- Cited by
- 325
- References
- 37
- FWCI
- 18.95
- Type
- journal article
- Language
- en
- Open access
- green
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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Hate speech detection: A solved problem? The challenging case of long tail on Twitter. https://app.cochat.ai/papers/10.3233/sw-180338
- The paper
- Zhang, Z., & Luo, L. (2019). Hate speech detection: A solved problem? The challenging case of long tail on Twitter. Semantic Web. https://doi.org/10.3233/sw-180338
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
