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Emotionally Informed Hate Speech Detection: A Multi-target Perspective.

Patricia Chiril1, Endang Wahyu Pamungkas2, Farah Benamara1

  • 1IRIT, Université de Toulouse, Université Toulouse III - UPS, Toulouse, France.

Cognitive Computation
|July 5, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a multi-target approach for hate speech detection, improving accuracy by considering specific topics and targets. Incorporating affective knowledge enhances the model

Keywords:
Affective resourcesHate speech detectionHate speech targetsMulti-task learningSocial media

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Area of Science:

  • Computational Linguistics
  • Natural Language Processing
  • Social Media Analysis

Background:

  • Online hate speech and harassment are prevalent, often targeting vulnerable groups based on characteristics like gender, ethnicity, religion, and sexual orientation.
  • Existing automatic hate speech detection methods typically use binary classification, failing to address the topical focus or target-specific nature of hate speech.
  • The anonymity and lack of regulation on social media platforms exacerbate the spread of hate speech.

Purpose of the Study:

  • To develop and evaluate a multi-target hate speech detection framework that goes beyond binary classification.
  • To investigate the transferability of knowledge across different hate speech datasets with varying topical focuses and targets.
  • To explore the impact of affective knowledge resources on fine-grained hate speech detection.

Main Methods:

  • Leveraged manually annotated datasets for hate speech detection.
  • Developed models for detecting both hate speech topics (e.g., racism, sexism) and targets.
  • Experimented with neural models, including multi-task learning approaches, and incorporated affective knowledge from SenticNet, EmoSenticNet, and HurtLex.

Main Results:

  • Training models on a combination of topic-specific datasets proved more effective than using a topic-generic dataset.
  • Multi-task learning models outperformed single-task models in detecting hatefulness and topical focus simultaneously.
  • Models incorporating affective knowledge (EmoSenticNet, SenticNet, HurtLex) achieved the best results in fine-grained hate speech detection.

Conclusions:

  • Multi-target hate speech detection is feasible using existing datasets, offering a pathway for detection when specific annotated data is scarce.
  • Integrating domain-independent affective knowledge significantly enhances the granularity and accuracy of hate speech detection.
  • The proposed multi-target approach represents a significant step towards more nuanced and effective online hate speech mitigation strategies.