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

arXiv: https://arxiv.org/abs/1906.11310 · HTML: https://mlsanity.com/paper/1906.11310/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Jun 2019.

## Built on by (most cited first, 23 of 23)

- [An Empirical Analysis on Large Language Models in Debate Evaluation](https://mlsanity.com/paper/2406.00050/) (2024)
- [I Beg to Differ: A study of constructive disagreement in online conversations](https://mlsanity.com/paper/2101.10917/) (2021)
- [Fair and Argumentative Language Modeling for Computational Argumentation](https://mlsanity.com/paper/2204.04026/) (2022)
- [Scientia Potentia Est -- On the Role of Knowledge in Computational Argumentation](https://mlsanity.com/paper/2107.00281/) (2021)
- [Social Influence Dialogue Systems: A Survey of Datasets and Models For Social Influence Tasks](https://mlsanity.com/paper/2210.05664/) (2022)
- [Prompting with Pseudo-Code Instructions](https://mlsanity.com/paper/2305.11790/) (2023)
- [Automatic Debate Evaluation with Argumentation Semantics and Natural Language Argument Graph Networks](https://mlsanity.com/paper/2203.14647/) (2022)
- [Towards Strategic Persuasion with Language Models](https://mlsanity.com/paper/2509.22989/) (2025)
- [A semantic embedding space based on large language models for modelling human beliefs](https://mlsanity.com/paper/2408.07237/) (2024)
- [ORCHID: A Chinese Debate Corpus for Target-Independent Stance Detection and Argumentative Dialogue Summarization](https://mlsanity.com/paper/2410.13667/) (2024)
- [An Item Response Theory Framework for Persuasion](https://mlsanity.com/paper/2204.11337/) (2022)
- [Towards a Perspectivist Turn in Argument Quality Assessment](https://mlsanity.com/paper/2502.14501/) (2025)
- [The Future of Cognitive Strategy-enhanced Persuasive Dialogue Agents: New Perspectives and Trends](https://mlsanity.com/paper/2402.04631/) (2024)
- [Argument Quality Assessment in the Age of Instruction-Following Large Language Models](https://mlsanity.com/paper/2403.16084/) (2024)
- [Sequence Graph Network for Online Debate Analysis](https://mlsanity.com/paper/2406.18696/) (2024)
- [Learning to Retrieve User History and Generate User Profiles for Personalized Persuasiveness Prediction](https://mlsanity.com/paper/2601.05654/) (2026)
- [Persona Knowledge-Aligned Prompt Tuning Method for Online Debate](https://mlsanity.com/paper/2410.04239/) (2024)
- [What Helps Language Models Predict Human Beliefs: Demographics or Prior Stances?](https://mlsanity.com/paper/2511.18616/) (2025)
- [SAD: A Large-Scale Strategic Argumentative Dialogue Dataset](https://mlsanity.com/paper/2601.07423/) (2026)
- [DEBISS: a Corpus of Individual, Semi-structured and Spoken Debates](https://mlsanity.com/paper/2603.05459/) (2026)
- [GRASP: Deterministic argument ranking in interaction graphs](https://mlsanity.com/paper/2605.19141/) (2026)
- [PTCG: Persona-guided Tree-based Counterargument Generation](https://mlsanity.com/paper/2609.07120/) (2026)
- [Understanding and Reducing Misalignment in Persuasion Optimization](https://mlsanity.com/paper/2610.16070/) (2026)

## 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.