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HatEval (SemEval 2019 Task 5: Multilingual Detection of Hate Speech Against Immigrants and Women in Twitter)

Introduced by Valerio Basile et al. in SemEval-2019 Task 5: Multilingual Detection of Hate Speech Against Immigrants and Women in Twitter6 Jun 2019 archive 2025-07-28

Hate Speech is commonly defined as any communication that disparages a person or a group on the basis of some characteristic such as race, color, ethnicity, gender, sexual orientation, nationality, religion, or other characteristics. Given the huge amount of user-generated contents on the Web, and in particular on social media, the problem of detecting, and therefore possibly limit the Hate Speech diffusion, is becoming fundamental, for instance for fighting against misogyny and xenophobia.

The proposed task consists in Hate Speech detection in Twitter but featured by two specific different targets, immigrants and women, in a multilingual perspective, for Spanish and English. The task will be articulated around two related subtasks for each of the involved languages: a basic task about Hate Speech, and another one where fine-grained features of hateful contents will be investigated in order to understand how existing approaches may deal with the identification of especially dangerous forms of hate, i.e. those where the incitement is against an individual rather than against a group of people, and where an aggressive behavior of the author can be identified as a prominent feature of the expression of hate. Participants will be asked to identify, on the one hand, if the target of hate is a single human or a group of persons, on the other hand, if the message author intends to be aggressive, harmful, or even to incite, in various forms, to violent acts against the target.

  • TASK A - Hate Speech Detection against Immigrants and Women: a two-class (or binary) classification where systems have to predict whether a tweet in English or in Spanish with a given target (women or immigrants) is hateful or not hateful.
  • TASK B - Aggressive behavior and Target Classification: where systems are asked first to classify hateful tweets for English and Spanish (e.g., tweets where Hate Speech against women or immigrants has been identified) as aggressive or not aggressive, and second to identify the target harassed as individual or generic (i.e. single human or group).

Benchmarks archive 2025-07-28

All 1 leaderboard whose dataset resolves to this page shown (sort by any header). "First row" is the archive's own first row at snapshot, in the archive's row order; nothing here re-ranks and metric direction is not asserted.

First row (archive order)PaperCode
Hate Speech Detection HatEval HateBERT Macro F1 0.494 HateBERT: Retraining BERT for Abusive Language Detection... tommasoc80/HateBERT 2 Compare

Papers archive 2025-07-28

1 shown of 1 paper with a leaderboard row on this dataset's benchmarks, newest first. The archive's own "papers using this dataset" list was never published, so this is the benchmark-backed subset; the archive's count for this dataset is 67. The Syntology column is from Syntology's graph (read 2026-09-24), stated per sample; it is not part of any archive number.

DateSamples run Syntology
HateBERT: Retraining BERT for Abusive Language Detection in English 1 2 23 Oct 2020 not harvested

Dataset loaders archive 2025-07-28

No loader listed in the archive.

Tasks archive 2025-07-28

License archive 2025-07-28

CC-BY-NC-4.0

Modalities archive 2025-07-28

Languages archive 2025-07-28

Variants archive 2025-07-28

  • HatEval

1 variant name, as the archive lists them.

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