Topics Winning Arguments (TWA)

Topics Winning Arguments (TWA) is a topic-organized extension of the Winning Arguments (WA) dataset, designed to support topic-aware analysis of persuasion in online discussions.

TWA is derived from Reddit’s r/ChangeMyView (CMV) subreddit and preserves the original structure, splits, and annotations of WA, while introducing explicit topic assignments obtained via neural topic modeling.

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📌 What is in the dataset?

Each data point corresponds to:

• a CMV original post (OP), and
• one or more argument pairs, where:
  • one argument successfully persuaded the OP (received a Δ),
  • the other is a closely matched unsuccessful argument.

Arguments are grouped by:

• topic
• train / test split
• CMV thread

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🧠 Topics

Arguments are clustered into four high-level topics using BERTopic:

1. Food and Culture
2. Religion and Ethical Debates
3. Economics and Politics
4. Gender, Sexuality, and Minority Rights

Topic modeling details, statistics, and examples are provided in the associated paper.

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📁 Dataset structure

TWA/
├── train/
│   ├── topic_<id>_<name>/
│   │   ├── <doc_id>/
│   │   │   ├── op.json
│   │   │   └── pairs.json
├── test/
│   └── ...
├── metadata/
│   ├── document_assignments.csv
│   └── topic_info.csv

File descriptions

op.json

Contains the original CMV post:

{
  "doc_id": "...",
  "op_user": "...",
  "op_title": "...",
  "op_text": "...",
  "pair_ids": [...],
  "topic_id": "...",
  "topic_name": "...",
  "split": "train"
}

pairs.json

A list of persuasive / non-persuasive argument pairs:

[
  {
    "pair_id": "p_XXXX",
    "success": "...",
    "unsuccess": "..."
  }
]

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🚀 How to use the dataset

Example (Python)

import json
from pathlib import Path

doc_dir = Path("TWA/train/topic_2_Religion_and_Ethical_Debates/t3_2ro9ux")

with open(doc_dir / "op.json") as f:
    op = json.load(f)

with open(doc_dir / "pairs.json") as f:
    pairs = json.load(f)

print(op["op_text"])
print(pairs[0]["success"])

The structure is compatible with:

• Hugging Face datasets
• PyTorch / TensorFlow pipelines
• custom evaluation scripts

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📚 Credits

Original datasets

• Winning Arguments Tan et al., Winning Arguments: Interaction Dynamics and Persuasion Strategies in Good-faith Online Discussions, WWW 2016.

• Change My View (CMV) Reddit community: https://www.reddit.com/r/changemyview/

TWA fully acknowledges and builds upon the original annotations and data collection efforts of these works.

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📜 License

This dataset is released under the Creative Commons Attribution–NonCommercial–NoDerivatives 4.0 International (CC BY-NC-ND 4.0) license.

You are free to:

• Share — copy and redistribute the material in any medium or format

Under the following terms:

• Attribution — You must give appropriate credit to the authors of TWA, provide a link to the license, and indicate if changes were made.
• NonCommercial — You may not use the material for commercial purposes.
• NoDerivatives — You may not distribute modified versions of the dataset.

No additional restrictions apply beyond those described in the license.

🔗 Full license text: https://creativecommons.org/licenses/by-nc-nd/4.0/

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🔎 Note on original data sources

TWA is derived from:

• the Winning Arguments dataset, and
• Reddit’s r/ChangeMyView (CMV) content.

Users of TWA are responsible for complying with the original terms and conditions of these sources where applicable.

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📝 Citation

If you use TWA, please cite:

@inproceedings{labruna2026detecting,
  title     = {Detecting Winning Arguments with Large Language Models and Persuasion Strategies},
  author    = {Labruna, Tiziano and Modzelewski, Arkadiusz and
               Satta, Giorgio and Da San Martino, Giovanni},
  booktitle = {Proceedings of the 19th Conference of the European Chapter
               of the Association for Computational Linguistics (EACL)},
  year      = {2026}
}

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📬 Contact

For questions, issues, or suggestions, please open a Hugging Face issue or contact the authors of the paper at tiz.labruna@gmail.com.