Rumor detection in social network based on user, content and lexical features

Emergence in the social network leads to the extensive and faster diffusion of news than conventional news channels. Verification of data is challenging due to massive information on a social network. Unverified information can be a rumor or fake news that causes damage to an individuals and organizations, revealing the harmful impact on humanity. Therefore, it is vital to combat rumor diffusion to minimize the adverse effects on society. Despite vigorous efforts to deal with this issue, researchers mainly focussed on temporal dynamics of posts and other features like a user, network, content-based, which demonstrate a moderate accuracy. The time series features are associated with an event that suppresses the other quality features related to each post. There is a scope for improvement in the accuracy, so this paper focuses on post-wise features such as user-based, content-based and lexical-based features along with post sequences. We proposed a framework that uses various essential features and combines two deep learning models. Word embedding is utilized with bidirectional long short-term memory (BiLSTM) and combined with post-wise features using a multilayer perceptron (MLP), which improves accuracy. The experiments on the real-world dataset of Twitter demonstrate a notable improvement in accuracy compared to state-of-the-art approaches.