# User Comment-Guided Cross-Modal Attention for Interpretable Multimodal Fake News Detection
###### Abstract  
In order to address the pressing challenge posed by the proliferation of fake news in the digital age, we emphasize its profound and harmful impact on societal structures, including the misguidance of public opinion, the erosion of social trust, and the exacerbation of social polarization. Current fake news detection methods are largely limited to superficial text analysis or basic text-image integration, which face significant limitations in accurately identifying deceptive information. To bridge this gap, we propose the UC-CMAF framework, which comprehensively integrates news text, images, and user comments through an adaptive co-attention fusion mechanism. The UC-CMAF workflow consists of four key subprocesses: multimodal feature extraction, cross-modal adaptive collaborative attention fusion of news text and images, cross-modal attention fusion of user comments with news text and images, and finally, input of fusion features into a fake news detector. Specifically, we introduce multi-head cross-modal attention heatmaps and comment importance visualizations to provide interpretability support for the model's predictions, revealing key semantic areas and user perspectives that influence judgments. Through the cross-modal adaptive collaborative attention mechanism, UC-CMAF achieves deep semantic alignment between news text and images and uses social signals from user comments to build an enhanced credibility evaluation path, offering a new paradigm for interpretable fake information detection. Experimental results demonstrate that UC-CMAF consistently outperforms 15 baseline models across two benchmark datasets, achieving F1 Scores of 0.894 and 0.909. These results validate the effectiveness of its adaptive cross-modal attention mechanism and the incorporation of user comments in enhancing both detection accuracy and interpretability. interpretable multimodal fusion; fake news detection; cross-modal attention; user comments; social media 2025157904  
16 June 202512 July 202512 July  
developing effective fake news detection and mitigation methods has become a critical research direction in the fields of artificial intelligence and machine learning [5; 6]. In recent years, with the widespread application of multimodal learning in information retrieval, its advantages of leveraging multiple data sources for complementary information have become increasingly prominent [7; 8]. From image-text matching to video captioning and sentiment analysis, multimodal methods have significantly improved task performance by extracting and fusing features from different modalities [9; 10]. In this context, extending multimodal learning to the fake news detection domain, particularly utilizing user comments as a key modality for analysis to improve accuracy in identifying fake news through social signals, holds significant research value and practical importance [11; 12]. Although fake news detection has developed various methods based on content, context, and social signals [13; 14], these methods are typically limited to superficial textual analysis or basic text-image integration, encountering significant obstacles in precisely locating deceptive content [15; 16]. Existing methods often fail to comprehensively capture the nuances and complexities of fake news, primarily manifested in several aspects: sophisticated fake news often employs subtle linguistic techniques that are difficult to detect through traditional textual analysis; visual content is frequently manipulated or taken out of context to support false narratives; fake news creators exploit cognitive biases and emotional triggers to enhance credibility; valuable insights provided by user comments and reactions are often underutilized in current detection systems [17; 18].