Unified Dual-view Cognitive Model for Interpretable Claim Verification (2021)

Abstract: Recent studies constructing direct interactions between the claim and each single user response (a comment or a relevant article) to capture evidence have shown remarkable success in interpretable claim verification. Owing to different single responses convey different cognition of individual users (i.e., audiences), the captured evidence belongs to the perspective of individual cognition. However, individuals’ cognition of social things is not always able to truly reflect the objective. There may be one-sided or biased semantics in their opinions on a claim. The captured evidence correspondingly contains some unobjective and biased evidence fragments, deteriorating task performance. In this paper, we propose a Dual-view model based on the views of Collective and Individual Cognition (CICD) for interpretable claim verification. From the view of the collective cognition, we not only capture the word-level semantics based on individual users, but also focus on sentence-level semantics (i.e., the overall responses) among all users and adjust the proportion between them to generate global evidence. From the view of individual cognition, we select the top- k articles with high degree of difference and interact with the claim to explore the local key evidence fragments. To weaken the bias of individual cognition-view evidence, we devise inconsistent loss to suppress the divergence between global and local evidence for strengthening the consistent shared evidence between the both. Experiments on three benchmark datasets confirm that CICD achieves state-of-the-art performance.

## introduction the problem of claim credibility has seriously affected the media ecosystem. research @xcite illustrates that the prevalence of 'fake news' has decreased trust in public institutions, and undermined democracy. meanwhile, 'massive infodemic' during covid-19 has taken a great toll on health-care systems and lives @xcite . therefore, how to verify the claims spread in networks has become a crucial issue. current approaches on claim verification could be divided into two categories: 1) the first category relies on traditional machine learning and deep learning methods to capture semantics @xcite , sentiments @xcite , writing styles @xcite , and stances @xcite ) from claim content, and meta-data features, such as user profiles @xcite for verification. such approaches could improve verification performance, but they are hard to make reasonable explanations for the verified results, i.e., where false claims go wrong; 2) to tackle this issue, many researchers further focus on interpretable claim verification (the second category) by establishing interactive models between claims and each individual relevant article (or comment) to explore coherent @xcite , similar @xcite , or conflicting @xcite semantics as evidence for verifying the false parts of claims. in interpretable claim verification, the majority of models construct interactions between claims and each single user response (i.e., a comment or a relevant article) to capture evidence, which could effectively learn some of errant aspects of false claims. due to different single responses reflect the cognition of different individual users, the evidence captured by these models is usually confined to individual cognition. however, individuals' cognition of social things is not always able to truly reflect the objective @xcite . owing to individuals are affected by factors such as emotional tendency @xcite , traditional beliefs (willard and norenzayan, 2017), and selectively capturing information @xcite , there are considerable differences in cognition of different individuals, and they are prone to cognitive bias, like primacy effect @xcite and halo effect @xcite , there may be one-sided or biased semantics in their expressed opinions. thus, the captured evidence also correspondingly contains some unobjective and biased evidence fragments, deteriorating task performance. for instance, as shown in figure 1 , facing a claim to be verified, different individual users (here, users are the normal users on social media, not journalists or professionals) have different reactions. r2 (i.e., response 2 or relevant article 2) and r3 released by users contain unreliable and biased information perceived by their individuals, which may lead to some misleading information being captured as evidence by existing interactive models. therefore, how to explore users' collective cognition on claims is a major challenge for interpretable claim verification. to address the deficiencies, we propose a unified dual-view model based on collective and individual cognition (cicd) for interpretable claim verification, which focuses on discovering global evidence and local key evidence, respectively, and then strengthens the consistent shared evidence between the both. specifically, to explore users' collective cognition to capture global evidence, we design collective cognition view-based encoder-decoder module (ced). ced develops claim-guided encoder that not only learns word-level semantics based on individual user, but also captures sentence-level semantics (i.e., the overall opinions) among all users. here, a relevant article (a response) released by an individual user is usually a sentence sequence, so all sentence-level semantics convey the overall opinions of all users. then, ced develops hierarchical attention decoder to generate global evidence by adjusting weights of word-level and sentence-level semantics. to further acquire the local key evidence based on individual cognition, we develop individual cognition view-based selected interaction module (isi) to screen representative top-k articles with high difference and interact with the claim to gain local key evidence fragments. to weaken the bias of individual cognition view and strengthen the consistent shared evidence between global and local evidence, we project inconsistent loss to suppress the divergence. experimental results not only reveal the effectiveness of cicd but also provide its interpretability. our contributions are summarized: ## related work automatic verification approaches rely on neural networks to extract content-based features, like semantics @xcite , sentiments @xcite , writing styles (przybyla, 2020), etc., and metadata-based features, like user profilesbased @xcite , comment-based (bovet and makse, 2019), etc., for verification. these methods could improve the accuracy of claim verification, but they are lack of interpretability for the verified results. to tackle this, interpretable claim verification has received great attention. its basic principle is to obtain queried, corrected, and rumorrefuted semantics from the articles (or comments) related to claims to interpret the credibility of claims. at present, the methods for this task generally focus on direct interactions between claims and relevant articles to identify their matching degree @xcite , consistency @xcite , implication @xcite , conflict @xcite , etc., to learn practical evidence. for instances, han @xcite and ehian @xcite ) learned implication relationships between claims and relevant articles to capture semantic conflicts as evidence, which reflected a certain interpretability. however, since all relevant articles are involved, the captured conflicts may be affected by some low-quality articles with noisy semantics, easily resulting in the invalidation of the evidence. in our model, we design isi module to screen all relevant articles to capture the valuable representative articles with differential semantics, so as to learn local key evidence fragments. in addition, some methods, such as gear @xcite and kgat @xcite , relied on graph-based networks to conduct semantic aggregation and reasoning on relevant articles, so as to capture global evidence. nevertheless, these models treat an entire article (at the sentence level) as a node and ignore the importance of word-level semantics in each article. to overcome these defects, our model constructs a hierarchical attention decoder to fuse sentence-level and word-level semantics for finely-grained generating global evidence. ## the proposed approach in this section, we introduce the details of cicd as illustrated in figure 2 . inputs and outputs for cognitive input representations, the inputs of ced are a claim sequence and the concatenation of its all relevant articles with the number of n, while the inputs of isi are a claim sequence and each relevant article. given any a sequence of length l words @xmath0, x 2 , ..., x l }, where each word x @xmath1. particularly, the length of each sequence in relevant articles is l and that of the claim sequence is p. thus, we obtain the representations of the i-th relevant article and the claim as x r @xmath2, x @xmath3, respectively. for the outputs of the model, the outputs of ced are the generated global evidence sequence of length o words @xmath4, g 2 , ..., g o }, where g t is the representation of the t-th generated word and o is the length of g. the outputs of isi are the integrated vector of top-k local key evidence fragments @xmath5; i 2 , ; ...; i k ], where ; is the concatenation operation. ## conclusion in this paper, we proposed a unified dual-view model based on the perspectives of collective and individual cognition for interpretable claim verification, which constructed collective cognition view-based encoder-decoder module to generate global evidence and designed individual cognition view-based selected interaction module to explore local key evidence segments. besides, we introduced inconsistent loss to penalize the disagreement between global and local evidence for promoting the capture of consistent shared evidence. experiments on three different widely used datasets demonstrated the effectiveness and interpretability of our model. in the future, we plan to expand the work as follows: 1) developing questioning mechanism to filter the suspicious evidence; and 2) integrating social cognition, psychology, and other interdisciplinary knowledge to improve the interpretability of claim verification.