Early Detection of Fake News via Structured Social Interaction Simulation and Hierarchical Cross-Modal Fusion
Source abstract
Abstract
The widespread dissemination and societal impact of fake news underscore the critical need for effective detection. Existing methods remain limited, as they often fail to learn joint representations from multi-modal data and rely heavily on complete social interaction signals. Such information is frequently unavailable in practice, especially during the early propagation stages. To address early fake news detection in social media, this paper proposes a hierarchical cross-modal fusion framework with structured LLM-simulated social interaction (HCF-LSIM). The framework employs a progressive cross-modal attention mechanism to systematically align semantic representations across multiple levels, integrating textual, thematic, and visual features. Additionally, HCF-LSIM designs an LLM-powered social interaction simulator that generates structured triplets from adapted user profiles, effectively compensating for missing real-time interaction data. Experiments on public benchmarks demonstrate strong performance, with accuracies of 93.5% on Weibo and 87.2% on X (formerly Twitter), ranking first on Weibo and second on Twitter.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Misinformation and Its Impacts · Spam and Phishing Detection · Big Data and Digital Economy
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Software, Dalian University of Foreign Languages, Dalian 116044, China
Research Center for Language Intelligence, Dalian University of Foreign Languages, Dalian 116044, China
Funding
the Applied Basic Research Project of Liaoning Province · the Promote scientific research cooperation with Canada, Australia, New Zealand and Latin America and High-level Talent Training Program of Ministry of Education of China · the General Program of the National Social Science Fund of China
References (48)
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