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Bias and comparison framework for abusive language datasets.

Maximilian Wich1, Tobias Eder1, Hala Al Kuwatly1

  • 1Technical University of Munich, Munich, Germany.

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Summary
This summary is machine-generated.

Researchers created a framework to deeply compare abusive language datasets, addressing inconsistencies in data collection and labeling for better automatic hate speech detection models.

Keywords:
Abusive language detectionArabicBiasEnglishHate speech detection

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Area of Science:

  • Natural Language Processing
  • Computational Social Science
  • Data Science

Background:

  • Increased research in automatic abusive language and hate speech detection has led to diverse datasets.
  • Existing datasets vary significantly in context, platform, sampling, collection, and labeling, hindering comparability.
  • Previous dataset surveys offer only superficial comparisons, lacking in-depth analysis.

Purpose of the Study:

  • To develop a comprehensive framework for analyzing and comparing abusive language datasets.
  • To provide an in-depth comparison of five English and six Arabic abusive language datasets.
  • To enhance awareness among researchers and data scientists regarding dataset properties.

Main Methods:

  • Development of a novel bias and comparison framework tailored for abusive language datasets.
  • In-depth analysis of eleven datasets (five English, six Arabic) using the developed framework.
  • Qualitative and quantitative assessment of dataset characteristics.

Main Results:

  • The framework facilitates a nuanced understanding of dataset biases and differences.
  • Identified significant variations across datasets in terms of context, sampling, and labeling.
  • Highlighted the need for careful dataset selection in abusive language detection research.

Conclusions:

  • The developed framework is a valuable tool for researchers working with abusive language datasets.
  • Awareness of dataset properties is crucial for building robust and generalizable hate speech detection models.
  • The framework promotes more rigorous and transparent dataset evaluation in the field.