Automatic rumor recognition for public health and safety: A strategy combining topic classification and multi-dimensional feature fusion

Abstract

With the COVID-19 outbreak, health-related rumors have attracted significant attention from governments and global society. These rumors often mislead the public through multimedia, amplifying their negative impact and potentially manipulating public health narratives. On social media, detecting these rumors faces unique challenges, especially for emerging health events. Existing detection algorithms struggle because they mainly learn event-specific features that are not applicable to new or unseen events. To overcome this, we developed an end-to-end framework called the Health Domain Multimodal Rumor Detection Neural Network (HDRNN). This framework extracts invariant features and effectively detects new health-related rumors. It consists of three components: a multimodal feature extractor, a rumor detector, and an event discriminator. The multimodal feature extractor extracts text and visual features from posts, working with rumor detectors to learn discriminative features. Event discriminator remove specific features while retaining shared ones across events. Extensive experiments on datasets from Tencent News and Sina Weibo show that our HDRNN model excels in multimodal health rumor detection, surpassing current methods.

Keywords

• Health-related rumors
• Health data
• Multi-scale word frequency
• High-level and low-level semantic fusion
• Image feature extraction
• Multimodal architecture