A Progressive Framework for Role-Aware Rumor Resolution (2022)

Abstract: Existing works on rumor resolution have shown great potential in recognizing word appearance and user participation. However, they ignore the intrinsic propagation mechanisms of rumors and present poor adaptive ability when unprecedented news emerges. To exploit the fine-grained rumor diffusion patterns and generalize rumor resolution methods, we formulate a predecessor task to identify triggering posts, and then exploit their characteristics to facilitate rumor verification. We design a tree-structured annotation interface and extend PHEME dataset with labels on the message level. Data analysis shows that triggers play a critical role in verifying rumors and present similar lingual patterns across irrelevant events. We propose a graph-based model considering the direction and interaction of information flow to implement role-aware rumor resolution. Experimental results demonstrate the effectiveness of our proposed model and progressive scheme.

## introduction with the expansion of the internet, online information tends to spread quickly and widely including fake news, misinformation and rumors, the last of which is defined as circulating stories unverifiable or deliberately false @xcite . especially in current situation with infectious epidemics and intensive international relationships, researchers have witnessed more than 900% growth in the number of english fact-checks during the covid-19 outbreak. @xcite . automatically verifying rumors has become an urgent need for individuals and society. conventional methods for rumor resolution depend on exploiting the evolutionary characteristics of content and spreaders @xcite . benefiting from various attention mechanisms, there is a growing tendency to retrieve evidential messages, indicative tokens and critical users to enhance interpretability @xcite @xcite . however, capturing patterns from historical records faces great challenges while transferring to unprecedented events as rumors evolve quickly and recur infrequently. besides, extracting features of malicious users skips over the immediate indication and allusive roles of the content itself. recently, researchers are dedicated to investigating the intrinsic mechanism of rumor propagation rather than linguistic or rhetorical features. @xcite reveal that it is the information novelty that stimulates discussion desire which makes rumors spread faster and deeper. @xcite manage to locate echo chamber members who tend to amplify rumor threads and find them responsible for viral propagation. n. zehmakan and galam (2020) divide rumor participants into 3 groups (seeds who adamantly convince the truth, agnostics who firmly reject and others) and analyze their roles during diffusion. inspired by these sociological findings, we propose to explore and model different roles of messages as rumor evolves. figure 1 illustrates three types of messages with triggering effects via a cascade instance drawn from pheme dataset @xcite . the source tweet seemingly reports objectively and amplifies discussion topics regard to a shooting event. after several rounds of retweet, someone presents an attitude of denying and asks for evidence. finally, a user comes out to clarify the real condition and confirm the falseness of the source. suchlike online discussion is widely existed, however, only a few messages present these critical roles and the majority are insignificant reposts and comments. accordingly, our goal is to identify triggers, i.e., messages that have prominent effects on rumor proliferation and dominate the judgment of cascade credibility. we also claim that identifying the role-aware propagation mode will contribute to rational and sound rumor verification. to practice the idea, we formulate the task of trigger identification and annotate all the messages in pheme to form a jointly labeled dataset. based on the well-adopted graph learning methods, we further put forward the ugrn (unsymmetric graph recurrent networks) framework by additionally considering the direction and interaction of information flow to simulate trigger effect. moreover, we devise the role-aware integration and warm-up strategy to facilitate rumor verification. our contributions are of three-folds: ## related work as our research focuses on technically resolving rumors in social media, we relate it with existing computational rumor resolution methods and our motivation for applying graph neural networks. ## proposed model based on observations in previous section, we propose the unsymmetric graph recurrent networks (ugrn) to identify triggers and progressively verify rumors. figure 4 illustrates the overall architecture which is composed of two components, the sharing grn layers of two tasks and the triggeraware prediction module.