Dual-stream self-attentive random forest for false information detection

The prevalence of online social media facilitates massive knowledge acquisition and sharing throughout the Web. Meanwhile, it inevitably poses the risk of generating and disseminating false information by both benign and malicious users. Despite there has been considerable research on false information detection from both the opinion-based and fact-based perspectives, they mostly focus on tailored solutions for a particular domain and carry out limited work on leveraging multi-faceted clues such as textual cues, behavioral trails, and relational connection. We propose a novel dual-stream attentive random forest that is capable of selecting clues of discriminative information from individuals, collective information (e.g., texts), and correlations of entities (e.g., social interactions) adaptively. In particular, we use an interpretive attention model for learning textual contents. The model treats the important and unimportant content differently when constructing the textual representation and employs a multilayer perceptron to capture the hidden complex relationships among features of side information. We further propose a unified framework for leveraging the above clues, where we use attentive forests to provide probabilistic distribution as predictions over the two learned representations, which are then leveraged to make a better estimation. We conduct extensive experiments on three real-world benchmark datasets for fake news and fake review detection. The results show our approach outperforms multiple baselines in the accuracy of detecting false information.