Verifying Rumors via Stance-Aware Structural Modeling

TL;DR

This paper introduces a stance-aware structural modeling approach that effectively captures conversation content, stance signals, and structural cues to improve rumor verification on social media, outperforming prior methods.

Contribution

It proposes a novel scalable model that encodes posts with stance signals, incorporates stance distribution and reply depth, and enhances rumor verification accuracy.

Findings

Outperforms prior methods in rumor veracity prediction

Effective for early detection of rumors

Demonstrates cross-platform generalization

Abstract

Verifying rumors on social media is critical for mitigating the spread of false information. The stances of conversation replies often provide important cues to determine a rumor's veracity. However, existing models struggle to jointly capture semantic content, stance information, and conversation strructure, especially under the sequence length constraints of transformer-based encoders. In this work, we propose a stance-aware structural modeling that encodes each post in a discourse with its stance signal and aggregates reply embedddings by stance category enabling a scalable and semantically enriched representation of the entire thread. To enhance structural awareness, we introduce stance distribution and hierarchical depth as covariates, capturing stance imbalance and the influence of reply depth. Extensive experiments on benchmark datasets demonstrate that our approach significantly outperforms prior methods in the ability to predict truthfulness of a rumor. We also demonstrate that our model is versatile for early detection and cross-platfrom generalization.