Abstract

"Rumor detection on social media has recently attracted significant attention. Due to the complex user group and lack of regulation, rumor-spreaders intentionally disseminate rumors to sway pub-lic opinion, severely harming the general interests. Existing approaches generally perform rumor detection by analyzing both image and text modalities, and pay less attention to the interaction behaviors in social media, which can assist in distinguishing rumors from normal information.Furthermore, the images associated with rumors are often inconsistent or manipulated, how to distinguish these different features and utilize them effectively has become crucial in prevent-ing the widespread dissemination of rumors. To address the aforementioned issues, we proposeCross-modal Ambiguity Learning with Heterogeneous Interaction Analysis (CAHIA) for rumor detection. Specially, we design a novel heterogeneous graph feature extractor to fully utilize the different types of behavioral patterns in social interaction networks, we design an frequency inception net to extract manipulated visual features and adopt different fusing strategies to detect various types of rumors according to the ambiguity between text and image. Finally, a hierarchical cross-modal fusing mechanism is used to simulate the process users view and determine the authenticity of posts. Extensive experiments results demonstrate that CAHIA outperforms state-of-the-art models on four large-scale datasets for rumor detection in social media."