Debunking Rumors on T witter with Tree Transformer (2020)

Abstract: Rumors are manufactured with no respect for accuracy, but can circulate quickly and widely by “word-of-post” through social media conversations. Conversation tree encodes important information indicative of the credibility of rumor. Existing conversation-based techniques for rumor detection either just strictly follow tree edges or treat all the posts fully-connected during feature learning. In this paper, we propose a novel detection model based on tree transformer to better utilize user interactions in the dialogue where post-level self-attention plays the key role for aggregating the intra-/inter-subtree stances. Experimental results on the TWITTER and PHEME datasets show that the proposed approach consistently improves rumor detection performance.

## introduction online rumor perhaps is one of the most prevalent social diseases in the era of social media. an immediate example we are witnessing is the unprecedented information disorder represented by various rumors, conspiracy theories, hoaxes, fake news, etc. in parallel with the worldwide pandemic of covid19. in different places, a number of people were hospitalized or even died for drinking bootleg alcohol to prevent coronavirous infection, resulting from a false rumor attack on gullible public claiming that "smoking, methanol or cocaine can cure for the virus" 1 . automatic rumor debunking is at the core of battle against such massive disorder of information especially in the midst of crisis. rumor debunking aims to determine the veracity of a given topic or a claim. fact-checking websites, such as snopes.com and politifact.com, employ manual verification and investigative journalism, which is prone to low efficiency and poor coverage. for automated approaches, prior studies focus on engineering or learning features from sequential microblog streams @xcite @xcite @xcite @xcite . more recently, structure-based learning based on structured neural networks are proposed to capture the interactive characteristics of rumor diffusion, such as tree kernel @xcite , recursive neural network @xcite and tree lstm model @xcite . @xcite proposed to model potential dependencies between any two microblog posts with the post-level self-attention networks (plan), which has achieved the state-of-the-art detection performance. the plan model essentially treats the input tweets as a fully connected graph, by assuming that a user may not be directed solely at the tweet being replied considering the content created could also be applicable to other tweets in the thread @xcite . also, the representation of posts is enhanced by leveraging the strength of transformer's encoding architecture. nevertheless, we argue that such full connection which ignores the specific targets of replies in the hierarchy could create salient issues on post representation learning, especially in the vein of relatively deep conversation or argument. meanwhile, other existing tree-structured models based on propagation trees @xcite or recursive trees @xcite tend to oversimplify user interaction by genuinely following the tree edges for post matching or encoding. to illustrate our intuition, figure 1 exemplifies the propagation structure of a (rumor) claim "mike pence caught on hot mic delivering empty boxes of ppe for a pr stunt". the plan model basically assumes each user directed at all the tweets in the thread, which may be true for a shallow tree where most of nodes respond to the root node. however, this is not the case when it comes to a tree hierarchy as figure 1 shows. it can be seen that accurate viewpoints is generally associated with the context of parent posts, e.g., x 21 support x 2 , but x 2 refute the source claim r, therefore x 21 believe that the claim is false even though it contains a non-rumor-indicative patten "be right". on the other hand, x 21 even has no context correlation with the nodes from another branch such as x 12 . but the plan model might brought unexpected errors in this case when linking x 21 with r (or x 12 ) when making fully pairwise comparison. to this end, we propose to enhance the representation by exploring the stances towards the same target utilizing the associated contextual information. the starting point of our approach is an observation: each post in the propagation tree may trigger a set of responsive tweets (such as x 1 → [x 11 , x 12 ] in figure 1 ), we define such unit as a subtree, which eventually compose the whole tree hierarchy. accordingly, we extend the conventional transformer's encoder into three variants, i.e., a bottom-up transformer, a top-down transformer, and a hybrid transformer model. more specifically, our models selectively attend over tweets in the same subtree. as a result, it can be expected that user's viewpoint can be fully captured based on the context of propagation path. meanwhile, inaccurate information in a subtree can be cross-checked as users share opinions towards the same target (i.e., the subtree root). we construct two shallow tree datasets and two deep tree datasets referring from two publicly benchmarks twitter and pheme. extensive experimental results demonstrate that our approach consistently improve over the state-of-the-art rumor detection and early detection baselines, particularly performing well on the deep trees. ## related work this section firstly reviews the recent progress about rumor detection. most previous automatic approaches for rumor detection @xcite @xcite intended to learn a supervised classifier by utilizing a wide range of features crafted from post contents, user profiles and propagation patterns. 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 alleviated the engineering effort by using a set of regular expressions (such as "really?", "not true", etc) to find questing and denying tweets, but the approach was oversimplified and suffered from very low recall. @xcite and @xcite respectively used recurrent neural networks (rnns) and convolutional neural networks (cnns) to learn automatically the representations from tweets content based on time series. @xcite proposed a hierarchical attention model which captures important clues from 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 the posts are propagated and requires careful data segmentation to prepare for the time sequence. some kernel-based methods were exploited to model the propagation structure. @xcite proposed a hybrid svm classifier which combines a rbf kernel and a random-walk-based graph kernel to capture both flat and propagation patterns for detecting rumors on sina weibo. @xcite used tree kernel to capture the similarity of propagation trees by counting their similar substructures in order to identify different types of rumors on twitter. @xcite presented tree-structured recursive neural networks (rvnn) to jointly generate the representation of a propagation tree based on the posts content and their propagation structure. in recent years, transformer @xcite have demonstrates state-of-the-art performance in a variety of nlp tasks such as machine translation @xcite , sentence representation @xcite , generative dialog @xcite , machine reading @xcite , semantic labeling @xcite , and rumor detection @xcite . transformer produce strong power of representations by applying attention to each pair of elements from an input sequence, regardless of their distance. @xcite propose a rumor verification model that allows direct modeling of dependencies between any two posts without regarding to their responsive relation, thus it essentially treats the propagation as a fully connected graph instead of a tree. our work is inspired by the idea of improving the representation power of transformer to model structured objects such as syntactic parse tree. in these works, a straightforward strategy is to augment the conventional transformer with structural positional embeddings @xcite . on the other hand, tree transformer is proposed to attend over nearer neighbor nodes @xcite . our proposed method is a substantial extension of tree transformer for modeling propagation tree structures for detecting rumors on microblogging websites. ## problem statement and notations on microblogging platforms such as twitter, the follower/friend relationship embeds shared interests among the users. once a user has posted a tweet, all his followers will receive it. twitter allows a user to retweet or comment on another user's post, so that the information could reach beyond the followers of the original creator. therefore, we model the propagation of each claim as a tree structure t (r) = v, e , where r is tree root representing the source tweet that states the claim, v refers to a set of nodes each representing a responding post of r in the thread of the circulation, and e is a set of directed edges corresponding to the response relation among the nodes in v . inspired by @xcite , here we consider two different propagation trees with distinct edge directions: (1) bottom-up tree where the responsive nodes point to their responded nodes, similar to a citation network; and (2) top-down tree where the edge follows the direction of information diffusion by reversing the bottom-up tree. we formulate this task as a supervised classification problem, which learns a classifier f from the labeled claims, that is, f : c @xmath0, where y i takes one of the four categories: non-rumor, true rumor, false rumor and unverified rumor (ntfu), that are introduced in previous literature @xcite . ## tree transformer model for rumor detection rumor indicative features can be captured from propagation structures, e.g., the stances expressed in responsive tweets can further reinforce the stances of that tweet is replying to @xcite , the posts with strong stance based on the tree branch is more important when determining the rumor veracity @xcite , and inaccurate information might be "self-checked" by making comparison with correlative tweets @xcite . however, such relation is not fully exploited by previous work. our core idea is to enhance representation learning of rumor indicative features by selectively attending over the corresponding tweets, that deeply explore user opinions and refine inaccurate information following the propagation tree structure. unlike the plan model that rawly handcraft 5 types of responsive relation as an additional consideration when attending over all the other tweets, our idea and the adopted mechanisms are significantly different. figure 2 gives an overview of our transformer-based framework respectively based on bottom-up tree and top-down tree, which will be depicted in detail in the subsections.