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Improved Abusive Comment Moderation with User Embeddings

arXiv 1708.03699 ↗First posted 11 Aug 2017cs.CLcited by 9

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Abstract (arXiv)

Experimenting with a dataset of approximately 1.6M user comments from a Greek news sports portal, we explore how a state of the art RNN-based moderation method can be improved by adding user embeddings, user type embeddings, user biases, or user type biases. We observe improvements in all cases, with user embeddings leading to the biggest performance gains.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). First posted on arXiv 11 Aug 2017.

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Citing papers per year: 2021: 5; 2022: 1; 2023: 1; 2024: 2.

9 papers.

  1. 2022Large-Scale Hate Speech Detection with Cross-Domain Transfercited by 39
  2. 2021Countering Online Hate Speech: An NLP Perspectivecited by 12
  3. 2023CoSyn: Detecting Implicit Hate Speech in Online Conversations Using a Context Synergized Hyperbolic Networkcited by 12
  4. 2021NLRG at SemEval-2021 Task 5: Toxic Spans Detection Leveraging BERT-based Token Classification and Span Prediction Techniquescited by 6
  5. 2021Toxicity Detection can be Sensitive to the Conversational Contextcited by 6
  6. 2021Modeling Users and Online Communities for Abuse Detection: A Position on Ethics and Explainabilitycited by 4
  7. 2024Putting Context in Context: the Impact of Discussion Structure on Text Classificationcited by 3
  8. 2021Not All Comments are Equal: Insights into Comment Moderation from a Topic-Aware Modelcited by 2
  9. 2024A Systematic Survey of Natural Language Processing for the Greek Languagecited by 1