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SemEval-2023 Task 10: Explainable Detection of Online Sexism

Summary

Most automated sexism detectors only say whether a post is sexist, without explaining why. This SemEval-2023 shared task, EDOS (Explainable Detection of Online Sexism), addresses that gap by organising detection into three hierarchical levels. Task A is a binary sexist/not-sexist decision. Task B assigns sexist posts to one of four categories. Task C assigns one of 11 fine-grained "vectors". The authors built the taxonomy from a review of earlier sexism and misogyny schemes, then refined it against real data using grounded theory. They released 20,000 labelled comments, split evenly between Gab and Reddit, along with larger unlabelled pools for model adaptation. All annotators were trained and self-identify as women, and experts settled their disagreements. The organisers also supplied baselines and analysed the participants' systems. The best-performing entries paired transformer architectures with further pre-training and simultaneous training on several related tasks. Fine-grained classification, however, remains far from solved. The resource matters because granular labels make moderation decisions easier to understand, contest and debug.

Key points

  • Taxonomy levels:
    • Task A covers binary sexism.
    • Task B has four categories: threats/incitement, derogation, animosity, and prejudiced discussion.
    • Task C has 11 vectors, such as "backhanded gendered compliments" and "condescending explanations".
  • Data sources:
    • Gab: 34M public posts (Aug 2016–Oct 2018), from which a random 1M pool was drawn.
    • Reddit: 81 candidate subreddits, restricted to 24 with at least 100k comments, giving about 42M comments.
    • 10k entries per platform were sampled for labelling.
  • Annotation: The annotators were UK-residing women who were trained through pilot rounds. Gold labels came from majority vote plus expert adjudication. Annotator IDs are released with each entry.
  • Out-of-scope abuse: Abuse aimed at targets other than women was flagged as "other target". This label applied to 2,098 of 15,146 non-sexist entries.
  • Availability and results: Data and guidelines are at github.com/rewire-online/edos. Top systems used transformers with domain-adaptive pre-training and multi-task setups.

Interesting ideas

  • I1. Hierarchy as explanation: Instead of building a separate explainability method, the label hierarchy itself (binary → category → vector) gives the "why". This approach could carry over to other moderation or classification tasks, such as propaganda or disinformation typing.
  • I2. Mutually exclusive, collectively exhaustive vectors: The fine-grained classes were designed to be distinct from each other and to cover all sexist content. This keeps multi-class annotation consistent and the label space complete, which is a useful check for any new taxonomy.
  • I3. Unlabelled pools alongside labelled data: Releasing large in-domain unlabelled corpora lets participants do continued pre-training. This offsets the financial and psychological cost of labelling, which is especially relevant for low-resource languages.
  • I4. Annotator-level transparency: Publishing annotator IDs alongside gold labels lets later work study disagreement and perspective effects instead of hiding them behind majority votes.
  • I5. Side labels for out-of-scope content: An extra flag, such as "other target", captures nearby phenomena that fall outside the task. This adds value to the data without blurring the main definition.

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: Natural language processing

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2023.semeval-1.305

@inproceedings{kirk2023semeval,
  title = {{SemEval-2023 Task 10: Explainable Detection of Online Sexism}},
  author = {Kirk, Hannah and Yin, Wenjie and Vidgen, Bertie and Röttger, Paul},
  booktitle = {Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)},
  year = {2023},
  doi = {10.18653/v1/2023.semeval-1.305},
  url = {https://doi.org/10.18653/v1/2023.semeval-1.305}
}

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