Towards Propagation Uncertainty: Edge-enhanced B ayesian Graph Convolutional Networks for Rumor Detection (2021)

Abstract: Detecting rumors on social media is a very critical task with significant implications to the economy, public health, etc. Previous works generally capture effective features from texts and the propagation structure. However, the uncertainty caused by unreliable relations in the propagation structure is common and inevitable due to wily rumor producers and the limited collection of spread data. Most approaches neglect it and may seriously limit the learning of features. Towards this issue, this paper makes the first attempt to explore propagation uncertainty for rumor detection. Specifically, we propose a novel Edge-enhanced Bayesian Graph Convolutional Network (EBGCN) to capture robust structural features. The model adaptively rethinks the reliability of latent relations by adopting a Bayesian approach. Besides, we design a new edge-wise consistency training framework to optimize the model by enforcing consistency on relations. Experiments on three public benchmark datasets demonstrate that the proposed model achieves better performance than baseline methods on both rumor detection and early rumor detection tasks.

## introduction with the ever-increasing popularity of social media sites, user-generated messages can quickly reach a wide audience. however, social media can also enable the spread of false rumor information @xcite . rumors are now viewed as one of the greatest threats to democracy, journalism, and freedom of expression. therefore, detecting rumors on social media is highly desirable and socially beneficial @xcite . almost all the previous studies on rumor detection leverage text content including the source tweet and all user retweets or replies. as time goes on, rumors form their specific propagation structures after being retweeted or replied to. @xcite ; @xcite have confirmed rumors spread significantly farther, faster, deeper, and more broadly than the truth. they provide the possibility of detecting rumors through the propagation structure. some works @xcite typically learn temporal features alone from propagation sequences, ignoring the internal topology. recent approaches @xcite ) model the propagation structure as trees to capture structural features. @xcite ; @xcite construct graphs and aggregate neighbors' features through edges based on reply or retweet relations. however, most of them only work well in a narrow scope since they treat these relations as reliable edges for message-passing. as shown in figure 1 , the existence of inaccurate relations brings uncertainty in the propagation structure. the neglect of unreliable relations would lead to severe error accumulation through multi-layer message-passing and limit the learning of effective features. we argue such inherent uncertainty in the propagation structure is inevitable for two aspects: i) in the real world, rumor producers are always wily. they tend to viciously manipulate others to create fake supporting tweets or remove opposing voices to evade detection @xcite . in these common scenarios, relations can be manipulated, which provides uncertainty in the propagation structure. ii) some annotations of spread relations are subjective and fragmentary @xcite . the available graph would be a portion of the real propagation structure as well as contain noisy relations, resulting in uncertainty. therefore, it is very challenging to handle inherent uncertainty in the propagation structure to obtain robust detection results. to alleviate this issue, we make the first attempt to explore the uncertainty in the propagation structure. specifically, we propose a novel edgeenhanced bayesian graph convolutional network (ebgcn) for rumor detection to model the uncertainty issue in the propagation structure from a probability perspective. the core idea of ebgcn is to adaptively control the message-passing based on the prior belief of the observed graph to surrogate the fixed edge weights in the propagation graph. in each iteration, edge weights are inferred by the posterior distribution of latent relations according to the prior belief of node features in the observed graph. then, we utilize graph convolutional layers to aggregate node features by aggregating various adjacent information on the refining edges. through the above network, ebgcn can handle the uncertainty in the propagation structure and promote the robustness of rumor detection. moreover, due to the unavailable of missing or inaccurate relations for training the proposed model, we design a new edge-wise consistency training framework. the framework combines unsupervised consistency training on these unlabeled relations into the original supervised training on labeled samples, to promote better learning. we further ensure the consistency between the latent distribution of edges and the distribution of node features in the observed graph by computing kldivergence between two distributions. ultimately, both the cross-entropy loss of each claim and the bayes by backprop loss of latent relations will be optimized to train the proposed model. we conduct experiments on three real-world benchmark datasets (i.e., twitter15, twitter16, and pheme). extensive experimental results demonstrate the effectiveness of our model. ebgcn of-fers a superior uncertainty representation strategy and boosts the performance for rumor detection. the main contributions of this work are summarized as follows: to the best of our knowledge, this is the first attempt to consider the inherent uncertainty in the propagation structure for rumor detection. ## problem statement this paper develops ebgcn which processes text contents and propagation structure of each claim for rumor detection. in general, rumor detection commonly can be regarded as a multi-classification task, which aims to learn a classifier from training claims for predicting the label of a test claim. formally, let @xmath0, c 2 , ..., c m } be the rumor detection dataset, where c i is the i-th claim and m is the number of claims. for each claim with the root node r i @xcite , where v @xmath1, x i 1 , x i 2 , ..., x i n i -1 } refers to the node set and e @xmath2, @xmath3, ..., n i -1} represent a set of directed edges from a tweet to its corresponding retweets. denote a @xmath4 besides, each claim c i is annotated with a ground-truth label y @xmath5, where y represents finegrained classes. our goal is to learn a classifier from the labeled claimed set, that is f : @xmath6. ## the proposed model in this section, we propose a novel edge-enhanced bayesian graph convolutional network (ebgcn) for rumor detection in section 4.2. for better training, we design an edge-wise consistency training framework to optimize ebgcn in section 4.3. ## conclusion in this paper, we have studied the uncertainty in the propagation structure from a probability perspective for rumor detection. specifically, we propose edge-enhanced bayesian graph convolutional networks (ebgcn) to handle uncertainty with a bayesian method by adaptively adjusting weights of unreliable relations. besides, we design an edge-wise consistency training framework incorporating unsupervised relation learning to enforce the consistency on latent relations. extensive experiments on three commonly benchmark datasets have proved the effectiveness of modeling uncertainty in the propagation structure. ebgcn significantly outperforms baselines on both rumor detection and early rumor detection tasks.