Multilevel Attention Residual Neural Network for Multimodal Online Social Network Rumor Detection

Abstract

In recent years, with the rapid rise of social networks, such as Weibo and Twitter, multimodal social network rumors have also spread. Unlike traditional unimodal rumor detection, the main difficulty of multimodal rumor detection is in avoiding the generation of noise information while using the complementarity of different modal features. In this article, we propose a multimodal online social network rumor detection model based on the multilevel attention residual neural network (MARN). First, the features of text and image are extracted by Bert and ResNet-18, respectively, and the cross-attention residual mechanism is used to enhance the representation of images with a text vector. Second, the enhanced image vector and text vector are concatenated and fused by the self-attention residual mechanism. Finally, the fused image–text vectors are classified into two categories. Among them, the attention mechanism can effectively enhance the image representation and further improve the fusion effect between the image and the text, while the residual mechanism retains the unique attributes of each original modal feature while using different modal features. To assess the performance of the MARN model, we conduct experiments on the Weibo dataset, and the results show that the MARN model outperforms the state-of-the-art models in terms of accuracy and F1 value.

Multi-criteria Evaluation of Social Media Platform’s Rumor Refuting Capacity Based on Fuzzy Theory and TOPSIS Method

“…At the same time, false news can be extensively distributed through numerous network channels [10]. Most Internet users, particularly on social media platforms, such as Weibo and Twitter, etc., have an access to a wider range of news at a lower cost, which simultaneously facilitates the spread of fake news or rumors [36]. According to the report issued by the Institute of Data Research of Nandu University, above 67 pieces of information per day are falsified by authoritative organizations on Weibo.…”

Echo chamber effects on short video platforms

“…This matching of information gradually creates a powerful driving force for group polarization, which is highly likely to lead to the formation of echo chambers. In addition, due to the nature of such short video platform that spreads information quickly and widely 1,2 , there have been attempts to disseminate misleading information 3 , false news 4 , or rumors 5 . According to recent studies, the echo chamber effect of social media can promote the spread of misleading information, fake news, and rumors [6][7][8] .…”