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Asthma Detection Research Based on Voice Signal Processing and Machine Learning
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Is hate speech detection the solution the world wants?

Sara Parker1, Derek Ruths1

  • 1School of Computer Science, McGill University, Montreal, QC H3A 0E9, Canada.

Proceedings of the National Academy of Sciences of the United States of America
|February 27, 2023
PubMed
Summary
This summary is machine-generated.

Automated hate speech detection is a key focus for machine learning (ML) researchers, but a significant disconnect exists with other stakeholders, risking progress in online safety.

Keywords:
hate speechmachine learningnatural language processing

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Area of Science:

  • Computational Social Science
  • Hate Speech Detection
  • Online Discourse Analysis

Background:

  • The machine learning (ML) community prioritizes automated hate speech detection for online behavior mitigation.
  • A notable gap exists between ML researchers' views and those of other key stakeholders regarding hate speech.
  • This disconnect may hinder the acceptance and adoption of automated detection tools.

Purpose of the Study:

  • To investigate how various stakeholders perceive the challenge of hate speech and the role of automated detection.
  • To analyze the discourse surrounding hate speech mitigation efforts by different groups.
  • To identify the extent of the disconnect between the ML community and other stakeholders.

Main Methods:

  • Development and application of a structured approach to dissect stakeholder discourses.
  • Analysis of discussions on hate speech from online platforms, governments, and non-profit organizations.
  • Comparative analysis of perspectives on automated hate speech detection.

Main Results:

  • A profound disconnect was identified between the computer science research community and other stakeholders concerning hate speech mitigation.
  • This divergence in understanding and approach poses a significant risk to progress in addressing online hate speech.
  • Stakeholder perspectives on the utility and implementation of automated hate speech detection vary considerably.

Conclusions:

  • Urgent steps are needed to bridge the gap between computational researchers and other stakeholders.
  • Fostering a unified, multistakeholder community is crucial for advancing online civil discourse.
  • Integrating diverse perspectives is essential for effective hate speech mitigation strategies.