mlsanityData to 14 Oct 2026

How to disagree well: Investigating the dispute tactics used on Wikipedia

arXiv 2212.08353 ↗First posted 16 Dec 2022cs.CL cs.AI cs.CYcited by 6

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Abstract (arXiv)

Disagreements are frequently studied from the perspective of either detecting toxicity or analysing argument structure. We propose a framework of dispute tactics that unifies these two perspectives, as well as other dialogue acts which play a role in resolving disputes, such as asking questions and providing clarification. This framework includes a preferential ordering among rebuttal-type tactics, ranging from ad hominem attacks to refuting the central argument. Using this framework, we annotate 213 disagreements (3,865 utterances) from Wikipedia Talk pages. This allows us to investigate research questions around the tactics used in disagreements; for instance, we provide empirical validation of the approach to disagreement recommended by Wikipedia. We develop models for multilabel prediction of dispute tactics in an utterance, achieving the best performance with a transformer-based label powerset model. Adding an auxiliary task to incorporate the ordering of rebuttal tactics further yields a statistically significant increase. Finally, we show that these annotations can be used to provide useful additional signals to improve performance on the task of predicting escalation.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 10 of its 45 references that are in the map. First posted on arXiv 16 Dec 2022.

Builds on

10 of 45 references.

References · 10

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  3. 2020Longformer: The Long-Document Transformercited by 3,413
  4. 2016Ex Machina: Personal Attacks Seen at Scalecited by 210
  5. 2018Conversations Gone Awry: Detecting Early Signs of Conversational Failurecited by 73
  6. 2019Trouble on the Horizon: Forecasting the Derailment of Online Conversations as they Developcited by 41
  7. 2013Large-scale Multi-label Text Classification - Revisiting Neural Networkscited by 35
  8. 2017Argument Strength is in the Eye of the Beholder: Audience Effects in Persuasioncited by 35
  9. 2021I Beg to Differ: A study of constructive disagreement in online conversationscited by 18
  10. 2016Conversational Markers of Constructive Discussionscited by 17

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 3; 2025: 2; 2026: 1.

6 papers.