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A time-robust group recommender for featured comments on news platforms.

Cedric Waterschoot1,2, Antal van den Bosch2

  • 1KNAW Meertens Instituut, Amsterdam, Netherlands.

Frontiers in Big Data
|June 5, 2024
PubMed
Summary

This study introduces a group recommender system to help news platforms automatically select high-quality comments. The system effectively ranks comments, even with changing news topics, aiding content moderation.

Keywords:
content moderationnatural language processingnews recommendationonline discussionsranking

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Area of Science:

  • Computer Science
  • Information Retrieval
  • Natural Language Processing

Background:

  • Content moderators face challenges in selecting high-quality comments for news platforms due to manual processes and platform growth.
  • Automated systems are needed to assist moderators in efficiently identifying and featuring valuable user comments.

Discussion:

  • Expert evaluation highlights the subjective nature of comment moderation, supporting the utility of comment recommendation systems.
  • The research emphasizes the value of recommending comments over simple classification for aiding human moderators.

Key Insights:

  • A group recommender system integrating comment data, user history, and contextual relevance achieves high ranking scores.
  • The best-performing models maintain ranking performance (mean NDCG@5 of 0.89) across changing article topics and platform growth.
  • Realistic evaluation scenarios using unseen online discussions from different years validate the models' effectiveness.

Outlook:

  • This work advances semi-automated content moderation techniques.
  • Further research can explore enhancing deliberation quality assessment in online discourse through improved recommendation algorithms.