HaSpeeDe3 at EVALITA 2023: Overview of the Political and Religious Hate Speech Detection task

MIRKO LAI, F. Celli, A. Ramponi, S. Tonelli, C. Bosco, V. Patti

Research output: Contribution to conferencePaperpeer-review

Abstract

The Hate Speech Detection (HaSpeeDe3) task is the third edition of a shared task on the detection of hateful content in Italian tweets. It differs from the previous editions while maintaining continuity in analysing and contrasting hate speech (HS) on social media. While HaSpeeDe and HaSpeeDe2 were focused on HS against immigrants, Muslims and Roms, HaSpeeDe3 explores hate speech in strong polarised debates, concerning in particular politics and religion. It is articulated in two different tasks: A) In-domain political hate speech detection and B) Cross-domain hate speech detection about political and religious tweets. Task A consists in two different subtasks for which participants i) can only use the provided textual content of the tweet, or ii) can additionally employ contextual information about the tweet and its author. In Task B, that consists in two subtasks, participants are allowed to use any kind of external data for detecting hate speech in tweets about i) politics and ii) religion. Six teams from both academia and industry participated in the evaluation, with a total of 13 submitted runs for Task A and 16 for Task B.
Original languageEnglish
Publication statusPublished - 2023
Event8th Evaluation Campaign of Natural Language Processing and Speech Tools for Italian. Final Workshop, EVALITA 2023 - ita
Duration: 1 Jan 2023 → …

Conference

Conference8th Evaluation Campaign of Natural Language Processing and Speech Tools for Italian. Final Workshop, EVALITA 2023
Cityita
Period1/01/23 → …

Keywords

  • Hate speech detection
  • polarised debates
  • political hate speech
  • religious hate speech
  • shared task
  • social media analysis

Fingerprint

Dive into the research topics of 'HaSpeeDe3 at EVALITA 2023: Overview of the Political and Religious Hate Speech Detection task'. Together they form a unique fingerprint.

Cite this