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A Survey on Combating Hate Speech through Detection and Prevention in English

Paper · 2024 · Prashant Kapil and Asif Ekbal

Summary

This survey reviews English-language research on fighting online hate speech. It covers both detecting such content and preventing it. On the data side, it describes how corpora are built: crawling Twitter, Facebook, Reddit, Gab and Wikipedia, usually by searching for negative keywords. Annotation follows binary, multi-class or hierarchical schemes, with quality reported through agreement coefficients. The authors then trace how text classifiers evolved, from surface n-gram features with SVM, LR and RF, through word embeddings with CNN, LSTM and GRU, to transformer encoders such as BERT. Lexicons serve as extra features along the way. Beyond text, the survey discusses multimodal detection, mostly of memes, using early or late fusion and vision transformers. On prevention, it covers style transfer that rewrites offensive posts and the generation of counter-narratives. It also summarizes metrics, bias mitigation, explainable approaches and work on mental-health effects. Its main contribution is an organized map of the field rather than new experiments. It notes that audio and video detection is barely explored, data is hard to share, and few systems are deployed.

Key points

  • Class balance varies widely across corpora. Datasets such as Davidson et al. 2017 and de Gibert et al. 2018 contain only about 6–20% hateful items, while Röttger et al. 2021 and Borkan et al. 2019 contain about 70–80%.
  • Labeling schemes have shifted away from a single layer toward hierarchies that additionally identify who is being targeted, with examples including Mandl et al. 2021, Basile et al. 2019 and the OffensEval task of Zampieri et al. 2019. There is also a growing trend toward span-level toxicity marking: Pavlopoulos et al. 2021, and Sarker et al. 2023a with about 20K comments.
  • Lexicons named in the survey:
    • Davidson et al.: 179 terms
    • HurtLeX: a multilingual lexicon across 53 languages
    • Olteanu et al.: 163 terms
    • Qian et al.: 2105 terms
    • Wiegand et al.: 1651 terms
  • In the shared tasks cited, more than 60% of submitted systems relied on transformer encoders.
  • Evaluation practice differs by task:
    • Unimodal detection is mostly scored with F1.
    • Multimodal detection is mostly scored with AUC-ROC.
    • Generation tasks use BLEU, ROUGE, METEOR and perplexity, plus judgments of relevance and effectiveness.

Interesting ideas

  • I1. Character n-grams catch deliberately disguised words like "fck" or "a$$hole" and reportedly predict better than token n-grams, so sub-word evidence matters wherever spelling is noisy or adversarial.
  • I2. Hierarchical labeling first decides whether a post is offensive, then whether it is targeted, then who the target is, which turns a flat label into structured judgments.
  • I3. Marking the exact toxic span instead of labeling the whole comment makes the decision checkable and locates the evidence.
  • I4. Rule By Example ties each decision to concrete exemplars through contrastive learning, so every rule comes with cases that justify it.
  • I5. Style transfer rewrites offensive text into a neutral version while trying to keep its content, and is judged on both content preservation and fluency.

For Mova

How these ideas could serve Mova, our small open language model — its files, stages and tests:

  • I1 (tokens): A character-level fallback in the tokenizer and lemmatizer could map obfuscated forms to their real lemmas, so the tagger and the parser see a known word instead of an unknown token.
  • I2 (frames): The prag crate could model an insult as a speech act whose target is filled as an explicit role in the frame, answering "who attacked whom" rather than giving only a yes/no flag.
  • I3 (frames): An offensive-speech frame could point to the specific UD subtree that triggered it, which keeps the judgment explainable and avoids any bag-of-words scoring.
  • I4 (learn): Self-induced repair rules could be stored together with positive and contrasting example sentences, which double as negative controls when the rules are re-evaluated.
  • I5 (back): The text → graph → text path could soften a sentence by changing only the evaluative nodes in the meaning graph and then regenerating, with BLEU-style overlap and perplexity as checks.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Fields, topics and concepts

📚 Shelves 2The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 works

🧭 Topics 3Research subjects and languages the work is about.English · 2,263 worksSentiment & emotion · 972 worksBias, fairness & safety · 517 works

Cite

Cite as Prashant Kapil and Asif Ekbal. 2024. A Survey on Combating Hate Speech through Detection and Prevention in English. Proceedings of the 21st International Conference on Natural Language Processing (ICON).

The paper itself, not this page. BibTeX from the library's record of the work.

@inproceedings{kapil2024survey,
  title = {{A Survey on Combating Hate Speech through Detection and Prevention in English}},
  author = {Prashant Kapil and Asif Ekbal},
  booktitle = {Proceedings of the 21st International Conference on Natural Language Processing (ICON)},
  year = {2024},
  url = {https://aclanthology.org/2024.icon-1.57/}
}

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