Uncertainty-aware Propagation Structure Reconstruction for Fake News Detection (2022)

Abstract: The widespread of fake news has detrimental societal effects. Recent works model information propagation as graph structure and aggregate structural features from user interactions for fake news detection. However, they usually neglect a broader propagation uncertainty issue, caused by some missing and unreliable interactions during actual spreading, and suffer from learning accurate and diverse structural properties. In this paper, we propose a novel dual graph-based model, Uncertainty-aware Propagation Structure Reconstruction (UPSR) for improving fake news detection. Specifically, after the original propagation modeling, we introduce propagation structure reconstruction to fully explore latent interactions in the actual propagation. We design a novel Gaussian Propagation Estimation to refine the original deterministic node representation by multiple Gaussian distributions and arise latent interactions with KL divergence between distributions in a multi-facet manner. Extensive experiments on two real-world datasets demonstrate the effectiveness and superiority of our model.

## introduction nowadays, fake news foot_0 has posed detrimental effects on individuals and society. for example, telecommunication towers were burned due to a conspiracy theory linking covid-19 with 5g technology @xcite . to help mitigate the negative effects caused by fake news, it's critical to develop automatic methods to detect fake news. existing works generally leverage the user interactions (e.g., retweet) and shared content in a social media conversation thread to detect fake news. the key principle behind such work is that users on social media share opinions, conjectures and evidence for checking fake news. some studies @xcite flatten the conversation in a chronological order to catch linguistic and temporal features from the propagation sequence, which does not make better use of the network properties. some works @xcite @xcite build the conversation thread with a tree structure to capture the structural patterns from the interactions of information propagation. driven by the success of graph neural networks @xcite , recent methods @xcite @xcite regard the conversation thread as a graph structure and aggregate informative neighbors to learn a good representation for detection. however, most methods usually assume that the propagation structure is deterministic and complete at some point. in the real world, it is often the case that each sample describes a partial propagation structure that includes some missing and unreliable interactions due to various reasons such as personal privacy protection and profit-driven social bots @xcite . this fact contributes to the propagation uncertainty issue and makes it challenging to discover effective structural patterns for fake news detection. @xcite learned relational bias to alleviate the negative effect of unreliable interactions. but they only focus on explicit interactions between a tweet and its direct retweets. thus, they still ignore some latent interactions that are not connected but may share similar stances that are useful to debunk false information. these vital but missing latent interactions in the social media conversation thread are also key to driving the propagation uncertainty issue. thus, how to model the propagation uncertainty issue and learn effective structure-property is a practical research topic to enhance fake news detection. an intuitive way is to reconstruct the original propagation structure to capture all possible interactions between posting nodes. we argue that, in the propagation, many retweets that subconsciously promote each other (such as similar stances or emotions). @xcite ; @xcite have shown the positive gains of implicit interactions between sibling retweets from the same tweet. beyond their assumptions, we make the attempt to investigate more potential interactions of all postings in the propagation structure, not limited to sibling retweets. besides, previous works @xcite @xcite usually measure interactions by learning deterministic embedding of each tweet, which may be insufficient to depict potential interactions accurately and comprehensively for uncertain propagation. therefore, it is desirable to study potential interactions from multiple underlying facets, which can reflect their fuzzy stances, emotions, and other factors. in this paper, we investigate a broader propagation uncertainty issue caused by missing and unreliable interactions. towards this issue, we develop a novel dual graph-based model, named uncertainty-aware propagation structure reconstruction (upsr), to adaptively learn accurate and diverse structural properties. specifically, inspired by @xcite , we first utilize deep graph convolutional networks to fully model long-range interactions in the original propagation. then, instead of directly using deterministic node representations for reconstruction, we design a novel gaussian propagation estimation to sample node representations from multiple gaussian distributions where the covariance enables the model to reduce noisy interactions. we measure the kullback-leibler (kl) divergence between distributions in a multi-facet manner to update the propagation structure. based on the reconstructed graph, we apply root-aware graph convolutional networks to aggregate features based on the learned latent interactions. upsr's dual graph structure can not only learn accurate structural information in the original propagation but also capture diverse structural patterns in the reconstructed propagation. finally, we exploit the dual-graph representation to identify fake news. we conduct extensive experiments on two realworld public datasets. the experimental results show that upsr significantly outperforms the stateof-the-art models, indicating the effectiveness for fake news detection. the core contributions of this paper are summarized as follows: ## related work in the literature, some works @xcite @xcite leverage user characteristics to assist detection. as user information is not allowed recorded in many cases, we mainly focus on detecting fake news based on text and propagation. text-based fake news detection approaches (mihalcea and strapparava, 2009) emphasize investigating the truthfulness of news content by extracting its textual features. early works relied on feature engineering to capture textual characteristics, e.g., topic features @xcite , writing styles and consistency @xcite . after the emergence of deep learning, many works @xcite @xcite apply various neural networks to automatically learn rich semantic or syntactic features from the source news and its retweets to detect fake news. propagation-based fake news detection approaches take advantage of the information related to the dissemination of a news article. many empirical studies @xcite have shown that compared to real news, fake news has deeper propagation structures, and reaches a wider audience. @xcite jointly learned the sequential effect of comments and correlation between source news and the corresponding comments. to capture structural propagation patterns, @xcite constructed a tree-structured neural network to model the propagation structure. khoo et al. (2020) adopted transformer @xcite to learn long-distance interactions. recently, @xcite regarded the propagation as a graph and applied two graph convolutional networks (gcns) @xcite to learn structural patterns from two distinct directed graphs. hu et al. (2021); lin et al. (2021) further explored multi-relational interactions in the propagation graph. @xcite focused on the propagation uncertainty and learned robust structural features. differences with existing models. 1) the aforementioned graph-based models @xcite are shallow structure, limiting to explore latent interactions in a deeper propagation. inspired by @xcite , we stack more graph layers to explore long-range interactions in propagation. 2) most approaches learn latent structural features on statics propagation trees/graphs. they may be disturbed by missing and unreliable behaviors easily, leading to a broader propagation uncertainty issue. this paper designs modules to reconstruct original propagation and explore more latent interactions from multiple facets. ## problem statement formally, let @xmath0, e) be a propagation structure, where @xmath1, c 1 , ..., c n } is a set of nodes representing the source news r and its retweets c 1 , ..., c n . e refers to a set of explicit interactive behaviors, e.g., retweet. define the embedding of the source news r as @xmath2, and that of a retweet c @xmath3, where d 0 is the dimensionality of textual features. each propagation is annotated with a ground-truth label y @xmath4, 1}. we formulate the fake news detection problem as a binary classification problem, i.e., each sample can be real (y @xmath5. fake news detection task can be seen as to learn a classifier f from the labeled set, i.e., f : @xmath6. ## the proposed model in this section, we propose a novel dual graphbased model, upsr, to fully model long-range dependencies in the original propagation and explore rich latent dependencies in the corresponding reconstructed propagation. ## experiments in this section, we experimentally evaluate the performance of our proposed model for fake news detection. ## discussion in this section, we conduct more experiemtns to further understand the performance of upsr. ## conclusion this paper has studied a broader propagation uncertainty issue in fake news detection. we propose a novel uncertainty-aware propagation structure reconstruction (upsr) to jointly model long-range and potential interactions in the uncertain propagation. gaussian propagation estimation (gpe) is developed to reconstruct latent propagation by adapting the inherent uncertain effect of retweets in the propagation. experiments conducted on two real-world benchmarks have shown that upsr outperforms recent detection methods. in the future, we will focus on improving the detection performance of our model in scenarios where training propagation data is limited.