Each Fake News is Fake in its Own Way: An Attribution Multi-Granularity Benchmark for Multimodal Fake News Detection
This addresses the need for more nuanced fake news detection in social media, though it is incremental as it builds on existing multimodal detection by adding attribution.
The authors tackled the problem of multimodal fake news detection by constructing a new dataset (AMG) that provides multi-granularity attribution labels instead of binary ones, and proposed a model (OUR) that achieved detection and attribution, with experimental results showing AMG is challenging and opens new research avenues.
Social platforms, while facilitating access to information, have also become saturated with a plethora of fake news, resulting in negative consequences. Automatic multimodal fake news detection is a worthwhile pursuit. Existing multimodal fake news datasets only provide binary labels of real or fake. However, real news is alike, while each fake news is fake in its own way. These datasets fail to reflect the mixed nature of various types of multimodal fake news. To bridge the gap, we construct an attributing multi-granularity multimodal fake news detection dataset \amg, revealing the inherent fake pattern. Furthermore, we propose a multi-granularity clue alignment model \our to achieve multimodal fake news detection and attribution. Experimental results demonstrate that \amg is a challenging dataset, and its attribution setting opens up new avenues for future research.