DisFact: Fact-Checking Disaster Claims
Abstract
The rapid proliferation of false information on the internet poses a significant challenge before, during, and after disasters, emphasizing the critical need for domain-specific automatic fact-checking systems. In this study, we introduce DisFact, a new fact-checking pipeline, and a dataset of disaster-related claims generated from the Federal Emergency Management Agency (FEMA) press releases and disaster declarations. Our retrieval method involves no model training, making it more efficient and less resource-intensive. It starts by breaking a lengthy document into sentences; we further apply embeddings to calculate the relevancy score between a claim and document pairs and then compute the similarity score between claims and sentences to rank the retrieved evidence(s). For claim verification, we utilize a deep learning approach that comprises a transformer-based embedding with a feedforward neural network. The experimental findings demonstrate that our fact-checking models achieve top performance on our custom disaster dataset. Furthermore, our models outperform other state-of-the-art models on FEVER and SciFact shared tasks, underscoring the effectiveness of our approach and its adaptability in handling longer documents and generalizing across diverse fact-checking datasets. DisFact signifies a pivotal advancement in automated fact-checking, emphasizing simplicity, accuracy, and computational efficiency. DisFact dataset and code are available on GitHub (DisFact Dataset and Code - https://github.com/abdul0366/DisFact).
Recommended Citation
A. Adesokan et al., "DisFact: Fact-Checking Disaster Claims," Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 15440 LNCS, pp. 421 - 437, Springer, Jan 2025.
The definitive version is available at https://doi.org/10.1007/978-981-96-0576-7_31
Department(s)
Computer Science
Keywords and Phrases
Claim Verification; Disaster; Fact-checking; Retrieval
International Standard Book Number (ISBN)
978-981960575-0
International Standard Serial Number (ISSN)
1611-3349; 0302-9743
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2025 Springer, All rights reserved.
Publication Date
01 Jan 2025
Comments
Missouri University of Science and Technology, Grant CNS-2219615