A Cross-Modal Rumor Detection Scheme via Contrastive Learning by Exploring Text and Image internal Correlations

To address these issues, this paper presents a novel cross - modal rumor detection scheme based on contrastive learning, namely the Multi - scale Image and Context Correlation exploration algorithm (MICC). Specifically, we design an SCLIP encoder to generate unified semantic embeddings for text and multi - s cale image patches through contrastive pretraining, enabling their relevance to be measured via dot - product similarity. Building upon this, a Cross - Modal Multi - Scale Alignment module is introduced to identify image regions most relevant to the textual sema ntics, guided by mutual information maximization and the information bottleneck principle, through a Top - K selection strategy based on a cross - modal relevance matrix constructed between the text and multi - scale image patches. Moreover, a scale - aware fusion network is designed to integrate the highly correlated multi - scale image features with global text features by assigning adaptive weights to image regions based on their semantic importance and cross - modal relevance. The proposed methodology has been exte nsively evaluated on two real - world datasets. The experimental results demonstrate that it achieves a substantial performance improvement over existing state - of - the - art approaches in rumor detection, highlighting its effectiveness and potential for practic al applications.