Rumor detection on social networks based on Temporal Tree Transformer.

ABSTRACT: The rapid propagation of rumors on social media can give rise to various social issues, underscoring the necessity of swift and automated rumor detection. Existing studies typically identify rumors based on their textual or static propagation structural information, without considering the dynamic changes in the structure of rumor propagation over time. In this paper, we propose the Temporal Tree Transformer model, which simultaneously considers text, propagation structure, and temporal changes. By analyzing observing the growth of propagation tree structures in different time windows, we use Gated Recurrent Unit (GRU) to encode these trees to obtain better representations for the classification task. We evaluate our model's performance using the PHEME dataset. In most existing studies, in

SUBMITTER: Wu S 

PROVIDER: S-EPMC11975086 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature