RATSD: Retrieval Augmented Truthfulness Stance Detection from Social Media Posts Toward Factual Claims
Zhengyuan Zhu, Zeyu Zhang, Haiqi Zhang, Chengkai Li · 2025
Social media provides a valuable lens for assessing public perceptions and opinions.This paper focuses on the concept of truthfulness stance, which evaluates whether a textual utterance affirms, disputes, or remains neutral or indifferent toward a factual claim.Our systematic analysis fills a gap in the existing literature by offering the first in-depth conceptual framework encompassing various definitions of stance.We introduce RATSD (Retrieval Augmented Truthfulness Stance Detection), a novel method that leverages large language models (LLMs) with retrieval-augmented generation (RAG) to enhance the contextual understanding of tweets in relation to claims.RATSD is evaluated on TSD-CT, our newly developed dataset containing 3,105 claimtweet pairs, along with existing benchmark datasets.Our experiment results demonstrate that RATSD outperforms state-of-the-art methods, achieving a significant increase in Macro-F1 score on TSD-CT.Our contributions establish a foundation for advancing research in misinformation analysis and provide valuable tools for understanding public perceptions in digital discourse.