Multimodal Dual Emotion with Fusion of Visual Sentiment for Rumor Detection
This addresses the problem of rumor detection for social media platforms by introducing visual emotion, which is novel but incremental as it builds on existing multimodal methods.
The paper tackles rumor detection by incorporating visual emotion from images alongside textual emotion, showing that this multimodal dual emotion feature improves detection efficiency and outperforms existing sentiment features on real datasets.
In recent years, rumors have had a devastating impact on society, making rumor detection a significant challenge. However, the studies on rumor detection ignore the intense emotions of images in the rumor content. This paper verifies that the image emotion improves the rumor detection efficiency. A Multimodal Dual Emotion feature in rumor detection, which consists of visual and textual emotions, is proposed. To the best of our knowledge, this is the first study which uses visual emotion in rumor detection. The experiments on real datasets verify that the proposed features outperform the state-of-the-art sentiment features, and can be extended in rumor detectors while improving their performance.