Modeling Evolution of Message Interaction for Rumor Resolution (2020)

Abstract: Previous work for rumor resolution concentrates on exploiting time-series characteristics or modeling topology structure separately. However, how local interactive pattern affects global information assemblage has not been explored. In this paper, we attempt to address the problem by learning evolution of message interaction. We model confrontation and reciprocity between message pairs via discrete variational autoencoders which effectively reflects the diversified opinion interactivity. Moreover, we capture the variation of message interaction using a hierarchical framework to better integrate information flow of a rumor cascade. Experiments on PHEME dataset demonstrate our proposed model achieves higher accuracy than existing methods.

## introduction with increasing openness of social media platforms, unverified messages can be easily disseminated from person to person and result in tremendous rumor cascades which expose huge threat to individuals and society. to resolve rumors, firstly we need to detect statements that are ambiguous at the time of posting, then explore how users share and discuss rumors and finally assess their veracity as true, false or unverified. this can be represented as a pipeline of sub-tasks, including rumor detection, stance classification and rumor verification @xcite . identifying and debunking rumors automatically has been extensively studied in the past few years. state-of-the-art approaches construct sequential representations following a chronological order and then utilize temporal features to capture dynamic signals @xcite @xcite . although the source content stays invariable, time-series modeling successfully locates modifiers who might import evidence to correct misinformation or stir up enmity to discredit truth @xcite . these models generate promising results, however, they ignore local interactions happened during the message diffusion which is deemed to be important for the identification of rumors. figure @xcite shows a rumor cascade which is identified as false for devilishly suspect ray radley's role in the appalling sydney siege. as can be seen, denial to false rumor tends to evoke affirmative replies which further confuses the factuality of the message. besides, disagreement and query towards descriptive statements are able to trigger drastic discussion and result in validity modification. although some researchers explore propagation structure of rumor proliferation @xcite , they typically rely on rough aggregation of locally successional messages. moreover, the evolution of message interaction depicts the global characteristic of rumor cascades which improves the performance of verification. figure 1 ( @xcite illustrates the intuition using statistics drawn from pheme dataset @xcite . it can be seen that denial tweets with supportive parent posts appear frequently in false rumors especially in an early stage, while unverified rumors constantly stimulate queries behind positive messages along with time. as rumor cascade evolves, with more dialogue context and auxiliary evidence, assessing the message credibility comprehensively becomes possible. figure 1 : an illustration of how interaction happens during the propagation of messages. in order to capture local interactive patterns and explore how interactivity dominates global factuality judgment , we propose to learn conversational message interaction and cooperate with propagation structure to improve the performance of rumor resolution. to model message interaction, we learn the latent interactive pattern for a repost toward its original post via discrete variational autoencoders (dvaes) which has shown great potential in learning categorical latent patterns (interaction patterns in our case). for rumor resolution, latent variables not only represent participant's attitude, but can also control how much literal information is reserved for claim confirmation. we then employ an attention-based hierarchical architecture to capture temporal variation of message interaction. our contributions are of three-folds: ## related work our research is related to two areas including rumor resolution and application of discrete variational autoendocers. ## proposed model resolution of rumor cascades can be formulated as a supervised classification problem. given a treestuctured twitter cascade c which corresponds to a root tweet r 0 and its relevant responsive tweets {r 1 , r 2 , ..., r t }, the goal is to recognize the stance of each tweet y s i as support, comment, deny or query, as well as determine the class of the cascade y v as true, false or unverified. from our dataset, for each tweet r i , its post time t i and parent post r p i from which it retweets is also available. our model is based on a hierarchical architecture which consists of two components: (1) interaction modeling which cooperates child post with its parents via dvaes to generate message interaction and (2) evolution capturing that employs attention-based recurrent neural networks to capture temporal variation and make prediction, as shown in figure 2 . ## conclusion and future work in this paper, we propose to model the evolution of message interaction for rumor resolution. the interaction pattern between post repost pairs is modeled via via discrete variational autoencoers. and an attention-based hierarchical architecture is employed to capture the evalution of message interactions. experimental results on pheme dataset show that our framework significantly outperforms the baselines for rumor verification. further analysis shows that dvaes is able to model interaction features for better interaction pattern identification. besides, a closer look at attention weights present that some specific types of interactions contribute more on rumor resolution. in the future, we would like to explore the task of interaction type classification to further analyze the influence of various interaction types on rumor resolution. in addition, it would be interesting to identify those change points along the timeline when misinformation emerges.