CL-ISR: A Contrastive Learning and Implicit Stance Reasoning Framework for Misleading Text Detection on Social Media

TL;DR

This paper introduces CL-ISR, a novel framework combining contrastive learning and implicit stance reasoning to enhance the detection of misleading social media texts, addressing linguistic complexity and implicit content for better accuracy.

Contribution

The paper presents a new framework that integrates contrastive learning with implicit stance reasoning to improve misleading text detection on social media.

Findings

Enhanced detection accuracy demonstrated in experiments

Effective capture of implicit stance and emotional manipulation

Improved semantic differentiation between truthful and misleading texts

Abstract

Misleading text detection on social media platforms is a critical research area, as these texts can lead to public misunderstanding, social panic and even economic losses. This paper proposes a novel framework - CL-ISR (Contrastive Learning and Implicit Stance Reasoning), which combines contrastive learning and implicit stance reasoning, to improve the detection accuracy of misleading texts on social media. First, we use the contrastive learning algorithm to improve the model's learning ability of semantic differences between truthful and misleading texts. Contrastive learning could help the model to better capture the distinguishing features between different categories by constructing positive and negative sample pairs. This approach enables the model to capture distinguishing features more effectively, particularly in linguistically complicated situations. Second, we introduce the implicit stance reasoning module, to explore the potential stance tendencies in the text and their relationships with related topics. This method is effective for identifying content that misleads through stance shifting or emotional manipulation, because it can capture the implicit information behind the text. Finally, we integrate these two algorithms together to form a new framework, CL-ISR, which leverages the discriminative power of contrastive learning and the interpretive depth of stance reasoning to significantly improve detection effect.