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Cross-lingual hate speech detection using domain-specific word embeddings.

Ayme Arango Monnar1, Jorge Perez Rojas2, Barbara Polete Labra1,3

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This study introduces a novel multilingual embedding model for hate speech detection, outperforming existing models in zero-shot cross-lingual scenarios. The research highlights common cross-lingual patterns in online hate speech expression.

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Area of Science:

  • Natural Language Processing (NLP)
  • Computational Linguistics
  • Social Network Analysis

Background:

  • Hate speech detection is challenging due to language and cultural nuances.
  • Existing NLP tools are primarily English-centric, limiting multilingual applications.
  • Effective cross-lingual transfer for hate speech detection remains an unmet need.

Purpose of the Study:

  • To develop an efficient multilingual hate speech detection approach.
  • To create the first multilingual embedding model specifically for hate speech detection.
  • To leverage limited cross-lingual resources for improved detection.

Main Methods:

  • Development of a specialized multilingual embedding model for hate speech.
  • Extensive comparative evaluation against general-purpose language models.
  • Zero-shot cross-lingual evaluation in a hate speech classification task.

Main Results:

  • The proposed specialized embeddings outperformed complex general-purpose models in most tested settings.
  • The model demonstrated effectiveness in classifying hate speech across languages without prior labeled data.
  • Qualitative analysis revealed new cross-lingual relationships in hate speech terminology.

Conclusions:

  • The developed multilingual embedding model is effective for cross-lingual hate speech detection.
  • Common patterns exist in how hate speech is expressed across different languages.
  • The model successfully captures these cross-lingual relationships, offering a significant advancement.