Abstract:
With the rapid development of social media platforms and the increasing scale of the social media data, rumor spreaders are increasingly utilizing multimedia content to a...Show MoreMetadata
Abstract:
With the rapid development of social media platforms and the increasing scale of the social media data, rumor spreaders are increasingly utilizing multimedia content to attract the attention and trust of news consumers. However, only relying on manual identification will consume a lot of manpower, material and financial resources. Therefore, automatic rumor detectors (i.e., using an efficient method to allow machines to automatically identify rumors) were born. However, most existing methods either only consider text features without utilizing information-rich image or social graph features, or only concatenate text features and image or social graph features, failing to fully explore the correlation between modalities, although they show good performance in rumor detection. At the same time, there are few ways to utilize all three modalities simultaneously. In this paper, we propose a novel Graph Attention Network with Cross-Modal Interaction (GANCI) method for Rumor Detection, which concurrently combines text, image and social graph features. Meanwhile, in GANCI, we designed a Feature Interaction Network to interact text, image and social graph features. Extensive experiments demonstrate the superiority of our model in comparison with the state-of-the-art baselines.
Date of Conference: 30 June 2024 - 05 July 2024
Date Added to IEEE Xplore: 09 September 2024
ISBN Information: