Topology Imbalance and Relation Inauthenticity Aware Hierarchical Graph Attention Networks for Fake News Detection (2022)

Abstract: Fake news detection is a challenging problem due to its tremendous real-world political and social impacts. Recent fake news detection works focus on learning news features from News Propagation Graph (NPG). However, little attention is paid to the issues of both authenticity of the relationships and topology imbalance in the structure of NPG, which trick existing methods and thus lead to incorrect prediction results. To tackle these issues, in this paper, we propose a novel Topology imbalance and Relation inauthenticity aware Hierarchical Graph Attention Networks (TR-HGAN) to identify fake news on social media. Specifically, we design a new topology imbalance smoothing strategy to measure the topology weight of each node. Besides, we adopt a hierarchical-level attention mechanism for graph convolutional learning, which can adaptively identify the authenticity of relationships by assigning appropriate weights to each of them. Experiments on real-world datasets demonstrate that TR-HGAN significantly outperforms state-of-the-art methods.

## introduction fake news is a news article that is intentionally and verifiably false and could mislead readers @xcite . the widespread of fake news can immensely affect cyberspace security and even social stability @xcite . for example, fake news "all of walmart's e-commerce stores would include a pay with litecoin option beginning october 1" 1 causes litecoin's prices surged by over 37% to @xmath044,000 @xcite , resulting in huge losses for many investors. various fake news detection methods are developed to classify whether the news is fake or not, which can be roughly divided into news contentbased methods and social context-based methods. existing news content-based methods @xcite @xcite @xcite typically focus on mining lexical and syntactic features @xcite @xcite from news contents, ignoring the rich structural information of news propagation. to address this limitation, social context-based methods @xcite @xcite tend to learn the feature representations by the structure information of news propagation graph (npg), where npg consists of multiple types of nodes (e.g., news, comments and users) and relationships (e.g., follower, retweet and friendship). @xcite . nodes representing fake objects (e.g., news or comments) and abnormal users are highlighted in red, while the unauthentic relations are represented by dotted lines. (a) an abnormal user u ser 1 forwards a true news n ews 3 , and he manipulates or tricks a normal user u ser 2 to create a fake supporting comment on a fake news n ews 1 , or he hacks a normal user u ser 3 to forward a fake news n ews 2 . (b) a fake news producer u ser 1 deletes real opposing comments produced by normal users. (c) an abnormal user u ser 1 follows many normal users to disguise himself. although these social context-based methods achieve promising performance, they have an illadvised assumption that impair the performance of fake news detection. specifically, they assume a piece of news connecting with a trustworthy user should also have high credit. however, in many cases, the relationships in npg can be manipulated by users and thus are unauthentic. for example, we present several types of unauthentic relations in figure 1 , where a fake news creator could manipulate other users to create fake comments to support the fake news, or delete real comments opposing the fake news. this leads to inaccurate npg with unauthentic propagation structure, which confuses existing social context-based methods to make an incorrect prediction. besides, npg usually has the problem of topology imbalance: the imbalance caused by the asymmetric and uneven topology of labeled nodes, where the decision boundaries are driven by the labeled nodes close to the topological class boundaries thus interfering with the model learning @xcite . ideally, the influence from labeled nodes should decay with the topology distance and also the node influence boundaries should be consistent with the true class boundaries. but the topology imbalance issue would cause the node influence boundaries to deviate from the true class boundaries, resulting in inaccurate results. nevertheless, most approaches neglect this issue in npg, interfering with the detection results. to tackle these issues, we propose a novel topology imbalance and relation inauthenticity aware hierarchical graph attention networks (tr-hgan) for fake news detection. we firstly design a topology smoothing strategy to measure the weights of labeled nodes to alleviate the topology imbalance issue. then we propose a hierarchical-level attention mechanism to identify the authenticity of relations by measuring the appropriate weights to each of them, which can effectively reduce the influence of the inauthentic relationships in npg. the main contributions of this paper are as follows: ## related work fake news detection challenges the usage of related information (such as text content, comments, propagation patterns, etc.) to distinguish whether a news article is fake or not. related works can be divided into two perspectives: i) news content-based methods; ii) social context-based methods. ## proposed model the overall architecture of tr-hgan is shown in figure 2 , which involves four main components: i) npg construction; ii) text embedding; iii) hierarchical graph attention network that consists of topology imbalance smoothing strategy and hierarchical-level attention strategy; and iv) fake news classification. next, we describe each part of tr-hgan in detail. ## conclusion in this paper, we have studied the unauthentic relations and topology imbalance issue in the news propagation structure from a weight learning perspective on fake news detection. we propose a topology imbalance and relation inauthenticity aware hierarchical graph attention networks (tr-hgan) to capture robust structural features. specifically, we design a topology imbalance smoothing strategy to address the problem of topology imbalance in npg by increasing the weight of nodes with great influence and decreasing the weight of nodes with week influence. besides, we develop a hierarchical graph attention mechanism for graph convolutional learning, which can adaptively measure the authenticity of the relationships by assigning appropriate weight to each relationship, they are talking about a missible but they don't know for sure. exactly,they don't know for...