Abstract: Traditional rumor detection methods still have many shortcomings,such as insufficient utilization of multi-modal information,failure to consider sample scarcity,high labeling costs,and domain shifts.Therefore,it cannot meet the demands.To address the issues of sample scarcity and domain changes,this paper proposes a new Active Learning-based Multi-modal Fusion Rumor detection model,called ALMF.ALMF designs a novel uncertainty query strategy enhanced by the propagation structure graph,which ensures that the samples selected through active learning have greater learning value and reduces the demand for sample labeling.Meanwhile,ALMF employs multi-modal data,fully integrating text features,image features,and propagation structure features.The complementary enhancement between different modal features improves the capability of rumor detection.ALMF is tested on the PHEME and WEIBO datasets.The results show that ALMF outperforms the compared models,achieving an accuracy improvement of 2% to 9%.Compared to active learning based on basic query strategies,ALMF achieves performance that is nearly equivalent to that of full sample utilization with only approximately 5% of the samples labeled.By employing a query strategy enhanced with propagation structure graphs and cross-modal enhancement fusion methods,the ALMF model successfully addresses the challenges associated with rumor detection in new domain events.

Key words: Rumor detection, Active learning, Multi-modal fusion, Co-attention, Propagation graph enhancement