A Fake News Detection Model Based on Capsule Networks and Collaborative Attention

Fake news may obscure the truth or mislead readers through subtle manipulations of textual content, such as modifying a few keywords or adjusting syntactic structures. Such local-level alterations are often difficult for detection models to capture, which undermines their overall performance. To address the limitations in processing fine-grained textual details, we propose a novel fake news detection framework—BCCU, which integrates a pre-trained language model, capsule networks, and a co-attention mechanism. Specifically, BCCU employs BERT to extract global semantic representations from news text, leverages Capsule Network to identify subtle local patterns, and synergistically fuses these two feature streams via a Co-Attention mechanism. Additionally, it incorporates User attributes as auxiliary features to further enhance detection accuracy. We evaluate the BCCU framework on three benchmark datasets—Twitter15, Twitter16, and Weibo—achieving accuracies of 0.864, 0.851, and 0.945, respectively, outperforming existing baseline models. The results demonstrate that by effectively combining global and local textual features and integrating user profile information, BCCU can robustly detect fake news even when relying solely on the unimodal text modality.