Bilinear Learning with Dual-Chain Feature Attention for Multimodal Rumor Detection

Zheheng Guo, Haonan Liu, Lijiao Zuo and Junhao Wen ()
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Zheheng Guo: School of Earth Resources, China University of Geosciences (Wuhan), Wuhan 430074, China
Haonan Liu: School of Electronic Information and Communication, Huazhong University of Science and Technology, Wuhan 430074, China
Lijiao Zuo: School of Bigdata and Software Engineering, Chongqing University, Chongqing 400044, China
Junhao Wen: School of Bigdata and Software Engineering, Chongqing University, Chongqing 400044, China

Mathematics, 2025, vol. 13, issue 11, 1-23

Abstract: The rapid growth of social media and online information-sharing platforms facilitates the spread of rumors. Accurate rumor detection to minimize manual verification efforts remains a critical research challenge. While multimodal rumor detection leveraging both text and visual data has gained increasing attention due to the diversification of social media content, existing approaches face the following three key limitations: (1) yhey prioritize lexical features of text while neglecting inherent logical inconsistencies in rumor narratives; (2) they treat textual and visual features as independent modalities, failing to model their intrinsic connections; and (3) they overlook semantic incongruities between text and images, which are common in rumor content. This paper proposes a dual-chain multimodal feature learning framework for rumor detection to address these issues. The framework comprehensively extracts rumor content features through the following two parallel processes: a basic semantic feature extraction module that captures fundamental textual and visual semantics, and a logical connection feature learning module that models both the internal logical relationships within text and the cross-modal semantic alignment between text and images. The framework achieves the multi-level fusion of text–image features by integrating modal alignment and cross-modal attention mechanisms. Extensive experiments on the Pheme and Weibo datasets demonstrate that the proposed method performs better than baseline approaches, confirming its effectiveness in detecting multimodal rumors.

Keywords: rumor detection; multimodal feature; modal alignment (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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