Rumor Detection on T witter with Claim-Guided Hierarchical Graph Attention Networks (2021)

Abstract: Rumors are rampant in the era of social media. Conversation structures provide valuable clues to differentiate between real and fake claims. However, existing rumor detection methods are either limited to the strict relation of user responses or oversimplify the conversation structure. In this study, to substantially reinforces the interaction of user opinions while alleviating the negative impact imposed by irrelevant posts, we first represent the conversation thread as an undirected interaction graph. We then present a Claim-guided Hierarchical Graph Attention Network for rumor classification, which enhances the representation learning for responsive posts considering the entire social contexts and attends over the posts that can semantically infer the target claim. Extensive experiments on three Twitter datasets demonstrate that our rumor detection method achieves much better performance than state-of-the-art methods and exhibits a superior capacity for detecting rumors at early stages.

## introduction rumor is one type of social diseases in the era of social media. the spread of false rumors has a far-reaching destructive impact on both society and individuals @xcite . for instance, the global covid-19 pandemic has created fertile soil for the widespread of various rumors, conspiracy theories, hoaxes, and fake news, heavily disrupting people's peaceful lives and leading to unprecedented information chaos. a strange, new rumor claiming that "wearing a mask to prevent the spread of covid-19 is unnecessary because the disease can also be spread via farts" 1 may mislead masses to belittle the importance of those potentially lifesaving masks in epidemic prevention. therefore, it is necessary to develop automatic approaches to facilitate rumor detection, especially amid crises. social psychology literature defines a rumor as a story or a statement whose truth value is unverified or intentionally false @xcite . rumor detection aims to determine the veracity of a given story or statement. for automating rumor detection, previous studies focus on text mining from sequential microblog streams with supervised classifiers based on feature engineering @xcite @xcite @xcite and feature learning @xcite . the interactions among users generally show conductive to provide useful clues for debunking rumors. structured information is generally observed on social media platforms such as twitter. structure-based methods @xcite are thus proposed to capture the interactive characteristics of rumor diffusion. we discuss briefly two types of stateof-the-art approaches: transformer-based @xcite and directed gcnbased @xcite models. @xcite exploited post-level selfattention networks to model long-distance interactions between any pair of tweets even irrelevant. @xcite further presented a treetransformer model to make pairwise comparisons among the posts in the same subtree hierarchically, which better utilizes tree-structured user interactions in the conversation thread. @xcite utilized graph convolutional networks (gcns) to encode directed conversation trees hierarchically. the structure-based methods however represent the conversation as a directed tree structure, following the bottom-up or top-down information flows. but such kind of structure, considering directed responsive relation, cannot enhance the representation learning of each tweet by aggregating information in parallel from the other informative tweets. in this paper, we firstly represent the conversation thread as an undirected interaction graph, which allows full-duplex interactions between x11: more than that, whatever he's doing, he accuses his opponents of doing. lying bigly for example. entail [r is true] contradict [r is false] contradict [r is false] contradict [r is false] contradict [r is false] contradict [r is false] posts with responsive parent-child or sibling relationships so that the rumor indicative features from neighbors can be fully aggregated and the interaction of user opinions can be reinforced. intuitively, we exemplify a false rumor claim and illustrate its propagation on twitter in figure 1 @xcite . we observe that a group of tweets is triggered to reply to the same post (i.e., parent post) in the conversation thread. as users share opinions, conjectures, and evidence, inaccurate information on social media can be "self-checked" by making a comparison with correlative tweets @xcite . in order to lower the weight of inaccurate responsive information (e.g., the supportive post x 2 toward the false claim r), coherent opinions need to be captured by comparing all responsive posts toward the same post. to achieve this, our proposed interaction topology as shown in figure 1 (b) takes the correlations between sibling nodes such as the dotted box portion into account. on the other hand, by leveraging the intrinsic structural property of graphbased modeling, the undirected graph allows each tweet to learn the representation by aggregating features from all its informative neighbors. in this way, information association between nodes in the conversation can be adaptively propagated to each other along the responsive parent-child or sibling relationships while avoiding the negative impact of irrelevant interactions such as the comparison between x 11 and x 21 in figure 1(a) . moreover, previous studies show that it is critical to strengthen the semantic inference capacity between posts and the claim based on textual entailment reasoning @xcite , so that we could semantically infer the claim by implicitly excavating textual inference relations such as entail, contradict, and neutral. we hypothesis that all the informative posts should be developed and extended around the content of the claim, i.e., the potential and implicit target to be checked. therefore, the claim content is significant to catch informative tweets, such as that in figure 1 (a), it is observed that x 22 satirizes the opinion expressed in x 2 , but its contextual information is limited. integrating claim information for claim-aware representations could not only enrich the semantic context of x 22 , but also enable it to better guard the consistency of topics when interacting with other nodes such as x 2 and x 21 . to this end, we propose a novel claim-guided hierarchical graph attention network (clahi-gat) for detecting rumors on twitter, which not only enhances the representation learning for posts by taking the entire conversation context but also attends over the subset of informative posts. more specifically, we firstly model the conversation thread of a claim as an undirected interaction graph. to flexibly deal with the interaction of node information and the association of the global structure of the graph, we propose clahi-gat to embed the undirected interaction graph. different from standard graph attention networks (gats) @xcite , we design a claim-guided hierarchical attention mechanism at both post and event level to attend over informative posts by considering the coherent attitude and semantic inference strength toward the claim. as a result, the post-level representation is enhanced by the claim-aware attention weights obtained based on the textual content of the claim. finally, we utilize an inference-based attention layer to implicitly capture the inference relation between the claim and the selected informative posts for rumor prediction at the event-level. we conduct extensive experiments on three pub-lic twitter datasets and demonstrate that our proposed clahi-gat model yields outstanding improvements over the state-of-the-art baselines with a large margin, and our method performs particularly well on early rumor detection which is crucial for timely intervention and debunking. the main contributions of this paper are three-fold: ## related work pioneer studies for automatic rumor detection focus on features crafted from post contents, user profiles, and propagation patterns to learn a supervised classifier @xcite @xcite . subsequent studies were then conducted to engineer new features such as those representing rumor diffusion and cascades @xcite @xcite . @xcite extended their model with a large set of chronological social context features. these approaches typically require heavy preprocessing and feature engineering. @xcite relieved the engineering effort by using a set of regular expressions (such as "really?", "not true", etc) to find questing and denying tweets, but the oversimplified approach suffered from very low recall. @xcite and @xcite respectively utilized recurrent neural networks (rnns) and convolutional neural networks (cnns) to learn the representations from tweets content based on time series. @xcite proposed a hierarchical attention model that captures important clues from the social context of a rumorous event at the post and sub-event levels. @xcite exploited the conflicting viewpoints in a credibility propagation network for verifying news stories propagated among the tweets. however, these approaches cannot embed features reflecting how posts are propagated and require careful data segmentation to prepare for time sequences. to extract useful clues jointly from content semantics and propagation structures, @xcite proposed a hybrid svm classifier to capture both flat and propagation patterns for detecting rumors on sina weibo. @xcite used tree kernel to capture the similarity of propagation trees in order to identify different types of rumors on twitter. @xcite presented treestructured recursive neural networks (rvnn) to jointly generate the representation of a propagation tree based on the post contents and their propagation structure. more recently, @xcite proposed to model potential dependencies between any two microblog posts with the post-level selfattention networks, which is too vulnerable to avoid the negative impact of interactions among irrelevant posts. ma and gao (2020) treated transformer as the unit of the tree structure to further enhance the representation learning but its running time is sensitive to conversation's depth. @xcite to encode the bi-directional conversation trees for higher-level representations. in recent years, gats have demonstrated superior performance in a variety of nlp tasks, such as text classification @xcite , machine reading @xcite , recommendation system @xcite , modeling knowledge graph @xcite and social network bias @xcite , etc. different from these previous works, in this paper, we attempt to learn graph attention-based embeddings that attend to user interactions from community response for rumor detection. ## problem statement we define a twitter rumor detection dataset as a set of events @xmath0, c 2 , ..., c |c| }, where each event c τ corresponds to a claim c, composed of ideally all its relevant responsive tweets in chronological order, i.e., c τ = {c, x 1 , x 2 , ..., x m }, where c can also be denoted as x 0 and m is the number of responsive tweets in the conversation thread. note that although the tweets are notated sequentially, there are connections among them based on their reply or repost relationships. so most previous works represent the conversation thread as a directed tree structure @xcite @xcite . we formulate the task of rumor detection as a supervised classification problem that learns a classifier f from the labeled claims, that is, f : c τ → y τ , where y τ takes one of the classes defined by the specific dataset: undirected interaction graphs construction. on twitter, each set of responsive posts triggered by the same post contains distinct rumor-indicative patterns @xcite . it is worth noting that we consider interactions not just between responsive parent-child nodes, but also those with the sibling relationship, for better feature aggregation from the informative tweets. to explore the full-duplex interaction patterns between responsive parent-child nodes or sibling nodes, we model the interaction topology among tweets as an undirected graph @xmath1, e for an undetermined event c τ , as exemplified in figure 1(b ), where @xmath2. for example, for any x i , x @xmath3, x @xmath4. ## claim-guided hierarchical graph attention networks in this section, we introduce our claim-guided hierarchical graph attention networks to embed the undirected interaction graph for rumor detection. the proposed neural network consists of two attention mechanisms, i.e., a graph attention to capture the importance of different neighboring tweets, and a claim-guided hierarchical attention to enhance post content understanding. figure 2 illustrates an overview of our proposed model, which will be depicted in the following subsections. ## conclusion in this paper, we propose a novel claim-guided hierarchical graph attention network based on undirected interaction graphs to learn graph attentionbased embeddings that attend to user interactions for rumor detection. multi-level rumor indicative features could be better captured via the claimaware attention at post level and the inferencebased attention at event level. the results on three public benchmark datasets confirm the advantages of our model. our framework is expected to provide new guidance for future rumor detection work.