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A Corpus for Modeling User and Language Effects in Argumentation on Online Debating

arXiv 1906.11310 ↗First posted 26 Jun 2019cs.CL cs.AIcited by 22

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Abstract (arXiv)

Existing argumentation datasets have succeeded in allowing researchers to develop computational methods for analyzing the content, structure and linguistic features of argumentative text. They have been much less successful in fostering studies of the effect of "user" traits -- characteristics and beliefs of the participants -- on the debate/argument outcome as this type of user information is generally not available. This paper presents a dataset of 78, 376 debates generated over a 10-year period along with surprisingly comprehensive participant profiles. We also complete an example study using the dataset to analyze the effect of selected user traits on the debate outcome in comparison to the linguistic features typically employed in studies of this kind.

Key facts. Cited by 22 papers in the map (data through 7 Oct 2026). First posted on arXiv 26 Jun 2019.

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Citing papers per year: 2021: 2; 2022: 4; 2023: 1; 2024: 7; 2025: 3; 2026: 5.

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  1. 2024An Empirical Analysis on Large Language Models in Debate Evaluationcited by 20
  2. 2021I Beg to Differ: A study of constructive disagreement in online conversationscited by 18
  3. 2022Fair and Argumentative Language Modeling for Computational Argumentationcited by 18
  4. 2021Scientia Potentia Est -- On the Role of Knowledge in Computational Argumentationcited by 17
  5. 2022Social Influence Dialogue Systems: A Survey of Datasets and Models For Social Influence Taskscited by 14
  6. 2023Prompting with Pseudo-Code Instructionscited by 13
  7. 2022Automatic Debate Evaluation with Argumentation Semantics and Natural Language Argument Graph Networkscited by 10
  8. 2024A semantic embedding space based on large language models for modelling human beliefscited by 9
  9. 2025Towards Strategic Persuasion with Language Modelscited by 7
  10. 2024ORCHID: A Chinese Debate Corpus for Target-Independent Stance Detection and Argumentative Dialogue Summarizationcited by 6
  11. 2022An Item Response Theory Framework for Persuasioncited by 5
  12. 2025Towards a Perspectivist Turn in Argument Quality Assessmentcited by 5
10 more
  1. 2024The Future of Cognitive Strategy-enhanced Persuasive Dialogue Agents: New Perspectives and Trendscited by 4
  2. 2024Argument Quality Assessment in the Age of Instruction-Following Large Language Modelscited by 4
  3. 2024Sequence Graph Network for Online Debate Analysiscited by 2
  4. 2026Learning to Retrieve User History and Generate User Profiles for Personalized Persuasiveness Predictioncited by 1
  5. 2024Persona Knowledge-Aligned Prompt Tuning Method for Online Debatecited by 0
  6. 2025What Helps Language Models Predict Human Beliefs: Demographics or Prior Stances?cited by 0
  7. 2026SAD: A Large-Scale Strategic Argumentative Dialogue Datasetcited by 0
  8. 2026DEBISS: a Corpus of Individual, Semi-structured and Spoken Debatescited by 0
  9. 2026GRASP: Deterministic argument ranking in interaction graphscited by 0
  10. 2026PTCG: Persona-guided Tree-based Counterargument Generationcited by 0