TA-BiLSTM: An Interpretable Topic-Aware Model for Misleading Information Detection in Mobile Social Networks

As essential information acquisition tools in our lives, mobile social networks have brought us great convenience for communication. However, misleading information such as spam emails, clickbait links, and false health information appears everywhere in mobile social networks. Prior studies have adopted various approaches to detecting this information but ignored global semantic features of the corpus and lacked interpretability. In this paper, we propose a novel end-to-end model called Topic-Aware BiLSTM (TA-BiLSTM) to handle the problems above. We firstly design a neural topic model for mining global semantic patterns, which encodes word relatedness into topic embeddings. Simultaneously, a detection model extracts local hidden states from text content with LSTM layers. Then, the model fuses those global and local representations with the Topic-Aware attention mechanism and performs misleading information detection. Experiments on three real datasets prove that the TA-BiLSTM could generate more coherent topics and improve the detecting performance jointly. Furthermore, case study and visualization demonstrate that the proposed TA-BiLSTM could discover latent topics and help in enhancing interpretability.