Verifying Rumors via Stance-Aware Structural Modeling
This work addresses the critical issue of mitigating false information spread on social media, representing an incremental improvement over existing models by better integrating stance and structural cues.
The paper tackled the problem of verifying rumors on social media by jointly capturing semantic content, stance information, and conversation structure, resulting in a model that significantly outperforms prior methods in predicting rumor truthfulness and demonstrates versatility for early detection and cross-platform generalization.
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.