Disinformation Propagation Trend Analysis and Identification Based on Social Situation Analytics and Multilevel Attention Network

Digital disinformation, such as those occurring on online social networks (OSNs), can influence public opinion, create mistrust and division, and impact decision- and policy-making. In this study, we propose a disinformation diffusion trend analysis and identification method, which uses social situation analytics and a multilevel attention network. First, we present a division and feature representation approach of social user circle based on the content sequence (internal driving factor) and social contextual information (external driving factor) of users associated with disinformation. Second, disinformation content feature, crowd response feature, and time-series feature are represented using embedding layer and bidirectional long short-term memory neural networks (Bi-LSTMs). We also present an attention mechanism model based on multifeature fusion, which can dynamically adjust the weight of each feature. On this foundation, the fused features are fed into the multilayer perceptron to identify the propagation quantity trend. According to the experimental results of real-world OSNs and social situation metadata, we conclude that while disinformation occurs across OSN platforms, the disinformation is more likely to spread widely in the original OSN platform. We also identify four typical disinformation propagation trends based on propagation patterns and propagation peak times. Findings from our experiments demonstrate that our proposed approach accurately identifies and predicts the diffusion trend of disinformation, which can then be used to inform mitigation strategy.

Automated summary

This study proposes a method for analyzing and identifying the diffusion trends of digital disinformation on online social networks. The method utilizes social situation analytics and a multilevel attention network to accurately identify and predict the spread of disinformation.