Abstract: Automatically verifying rumorous information has become an important and challenging task in natural language processing and social media analytics. Previous studies reveal that people's stances towards rumorous messages can provide indicative clues for identifying the veracity of rumors, and thus determining the stances of public reactions is a crucial preceding step for rumor veracity prediction. In this paper, we propose a hierarchical multi-task learning framework for jointly predicting rumor stance and veracity on Twitter, which consists of two components. The bottom component of our framework classifies the stances of tweets in a conversation discussing a rumor via modeling the structural property based on a novel graph convolutional network. The top component predicts the rumor veracity by exploiting the temporal dynamics of stance evolution. Experimental results on two benchmark datasets show that our method outperforms previous methods in both rumor stance classification and veracity prediction.

Introduction: Social media websites have become the main platform for users to browse information and share opinions, facilitating news dissemination greatly. However, the characteristics of social media also accelerate the rapid spread and dissemination of unverified information, i.e., rumors BIBREF0. The definition of rumor is “items of information that are unverified at the time of posting” BIBREF1. Ubiquitous false rumors bring about harmful effects, which has seriously affected public and individual lives, and caused panic in society BIBREF2, BIBREF3. Because online content is massive and debunking rumors manually is time-consuming, there is a great need for automatic methods to identify false rumors BIBREF4. Previous studies have observed that public stances towards rumorous messages are crucial signals to detect trending rumors BIBREF5, BIBREF6 and indicate the veracity of them BIBREF7, BIBREF8, BIBREF9, BIBREF10, BIBREF11. Therefore, stance classification towards rumors is viewed as an important preceding step of rumor veracity prediction, especially in the context of Twitter conversations BIBREF12. The state-of-the-art methods for rumor stance classification are proposed to model the sequential property BIBREF13 or the temporal property BIBREF14 of a Twitter conversation thread. In this paper, we propose a new perspective based on structural property: learning tweet representations through aggregating information from their neighboring tweets. Intuitively, a tweet's nearer neighbors in its conversation thread are more informative than farther neighbors because the replying relationships of them are closer, and their stance expressions can help classify the stance of the center tweet (e.g., in Figure FIGREF1, tweets “1”, “4” and “5” are the one-hop neighbors of the tweet “2”, and their influences on predicting the stance of “2” are larger than that of the two-hop neighbor “3”). To achieve this, we represent both tweet contents and conversation structures into a latent space using a graph convolutional network (GCN) BIBREF15, aiming to learn stance feature for each tweet by aggregating its neighbors' features. Compared with the sequential and temporal based methods, our aggregation based method leverages the intrinsic structural property in conversations to learn tweet representations. After determining the stances of people's reactions, another challenge is how we can utilize public stances to predict rumor veracity accurately. We observe that the temporal dynamics of public stances can indicate rumor veracity. Figure FIGREF2 illustrates the stance distributions of tweets discussing $true$ rumors, $false$ rumors, and $unverified$ rumors, respectively. As we can see, $supporting$ stance dominates the inception phase of spreading. However, as time goes by, the proportion of $denying$ tweets towards $false$ rumors increases quite significantly. Meanwhile, the proportion of $querying$ tweets towards $unverified$ rumors also shows an upward trend. Based on this observation, we propose to model the temporal dynamics of stance evolution with a recurrent neural network (RNN), capturing the crucial signals containing in stance features for effective veracity prediction. Further, most existing methods tackle stance classification and veracity prediction separately, which is suboptimal and limits the generalization of models. As shown previously, they are two closely related tasks in which stance classification can provide indicative clues to facilitate veracity prediction. Thus, these two tasks can be jointly learned to make better use of their interrelation. Based on the above considerations, in this paper, we propose a hierarchical multi-task learning framework for jointly predicting rumor stance and veracity, which achieves deep integration between the preceding task (stance classification) and the subsequent task (veracity prediction). The bottom component of our framework classifies the stances of tweets in a conversation discussing a rumor via aggregation-based structure modeling, and we design a novel graph convolution operation customized for conversation structures. The top component predicts rumor veracity by exploiting the temporal dynamics of stance evolution, taking both content features and stance features learned by the bottom component into account. Two components are jointly trained to utilize the interrelation between the two tasks for learning more powerful feature representations. The contributions of this work are as follows. $\bullet $ We propose a hierarchical framework to tackle rumor stance classification and veracity prediction jointly, exploiting both structural characteristic and temporal dynamics in rumor spreading process. $\bullet $ We design a novel graph convolution operation customized to encode conversation structures for learning stance features. To our knowledge, we are the first to employ graph convolution for modeling the structural property of Twitter conversations. $\bullet $ Experimental results on two benchmark datasets verify that our hierarchical framework performs better than existing methods in both rumor stance classification and veracity prediction.

Question: How much improvement does their model yield over previous methods?
Answer: For single-task, proposed method show
outperform by  0.031 and 0.053 Macro-F1 for SemEval and PHEME dataset respectively.
For multi-task, proposed method show
outperform by 0.049 and 0.036 Macro-F1 for SemEval and PHEME dataset respectively.