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.

Multimodal fake news detection has obtained increasing attention recently. Existing works generally encode multimodal contents into a deterministic point in semantic subspaces, and then fuse multimodal features by simple concatenation or attention mechanisms. However, most methods suffer from adapting to noisy multimodal contents since they neglect the robustness of modality-specific features. Besides, as different modalities usually have varying confidence levels, previous attention-based fusion models that learn modality-independent weights based on the input data feature, would limit the optimal integration of multimodal contents. To alleviate the above issues, we propose novel Multimodal Uncertainty Learning Network (MM-ULN) to enhance multimodal fake news detection by modeling both intra- and inter-modality uncertainty. Specifically, we incorporate a novel intra-modality uncertainty learning (EUL) module to better understand noisy multimodal contents. EULs provide feature regularization in a variational way, successfully alleviating the effects of data uncertainty within modalities. We design a new variational attention fusion (VAF) module to adaptively fuse multimodal contents with modality-dependent weights. The VAF module consider the relative confidence between modalities and enables to explore complementary properties for detection. Extensive experiments on two benchmark datasets demonstrate the effectiveness and superiority of MM-ULN on multimodal fake news detection.

Dou Hu, Xiaolong Hou, Xiyang Du, Mengyuan Zhou, Lianxin Jiang, Yang Mo, Xiaofeng Shi. Findings of the Association for Computational Linguistics: EMNLP 2022. 2022.