metadata
license: cc-by-4.0
language:
- en
tags:
- false-information
- hurricane
- wildfire
- climate
- disaster
- social-media
- Reddit
- multimodal
- information-integrity
size_categories:
- n<1K
FSD: False-Info Severity Detection
Dataset for the paper "Beyond Truth Discovery: A Two-Stage Framework to Assess the Severity of False Claim during Disasters". This dataset contains false claims extracted by an agent from disaster-related Reddit posts. The two CSV files cover hurricanes (hurricane_gt.csv) and wildfires (wildfire_gt.csv). Each row represents one extracted claim, rather than a full post.
| Column | Description |
|---|---|
claim_id |
Claim identifier. |
false_claim |
Agent-extracted false claim. |
believe_mv |
Believability label from majority voting by three independent annotators: 1 = low, 2 = high. |
harm_mv |
Harmfulness label from majority voting by three independent annotators: 1 = low, 2 = high. |
severity |
Combination of believe_mv and harm_mv, written in that order: 11, 12, 21, or 22. |
Believability measures how likely a claim is to be perceived as credible.
Harmfulness measures its potential consequences if believed.
Severity is derived from these two labels; it is not a separate annotation.
Citation
@inproceedings{yao2026fsd,
author = {Yao, Ruichen and Dasari, Tejna and Baispay, Gulshat and Zaurbek, Aizhan and Liu, Yifan and Liu, Yaokun and Li, Zelin and Wang, Dong},
title = {Beyond Truth Discovery: A Two-Stage Framework to Assess the Severity of False Claim during Disasters},
year = {2026},
isbn = {9798400728945},
url = {https://doi.org/10.1145/3834580.3838746},
doi = {10.1145/3834580.3838746},
booktitle = {Proceedings of the 2026 ACM Conference on Human-AI Complementarity and Alignment},
pages = {154–167},
numpages = {14},
}
Dataset DOI: 10.57967/hf/10877