Modeling the Uncertainty of Information Propagation for Rumor Detection: A Neuro-Fuzzy Approach

Automatic rumor detection is critical for maintaining a healthy social media environment. The mainstream methods generally learn rich features from information cascades by modeling the cascade as a tree or graph structure where edges are built based on interactions between a tweet and retweets. Some psychology studies have empirically shown that users’ various subjective factors always cause the uncertainty of interactions such as differences among interactive behavior activation thresholds or semantic relevancy. However, previous works model interactions by employing a simple fully connected layer on fixed edge weights in the graph and cannot reasonably describe this inherent uncertainty of complex interactions. In this article, inspired by the fuzzy theory, we propose a novel neuro-fuzzy method, fuzzy graph convolutional networks (FGCNs), to sufficiently understand uncertain interactions in the information cascade in a fuzzy perspective. Specifically, a new strategy of graph construction is first designed to convert each information cascade into a heterogeneous graph structure with the consideration of explicit interactive behaviors between a tweet and its retweet, as well as implicit interactive behaviors among retweets, enriching more structural clues in the graph. Then, we improve graph convolutional networks by incorporating edge fuzzification (EF) modules. The EFs adapt edge weights according to predefined membership to enhance message passing in the graph. The proposed model can provide a stronger relational inductive bias for expressing uncertain interactions and capture more discriminative and robust structural features for rumor detection. Extensive experiments demonstrate the effectiveness and superiority of FGCN on both rumor detection and early rumor detection.