Datasets:

tizaino
/

TWA

pretty_name: Topics Winning Arguments (TWA)
language:
  - en
license: cc-by-nc-nd-4.0
task_categories:
  - text-classification
  - text-generation
  - multiple-choice
tags:
  - argument-mining
  - persuasion
  - persuasion-detection
  - stance-detection
  - pairwise-comparison
  - social-media
  - reddit
  - changemyview
  - topic-modeling
  - bertopic
  - datasets
  - text
arxiv: arXiv-2601.10660

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.

📌 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

🧠 Topics

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

Food and Culture
Religion and Ethical Debates
Economics and Politics
Gender, Sexuality, and Minority Rights

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

📁 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": "..."
  }
]

🚀 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

📚 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.

📜 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/

🔎 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.

📝 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}
}

📬 Contact

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