GCAN : Graph-aware Co-Attention Networks for Explainable Fake News Detection on Social Media (2020)

Abstract: This paper solves the fake news detection problem under a more realistic scenario on social media. Given the source short-text tweet and the corresponding sequence of retweet users without text comments, we aim at predicting whether the source tweet is fake or not, and generating explanation by highlighting the evidences on suspicious retweeters and the words they concern. We develop a novel neural network-based model, Graph-aware Co-Attention Networks (GCAN), to achieve the goal. Extensive experiments conducted on real tweet datasets exhibit that GCAN can significantly outperform state-of-the-art methods by 16% in accuracy on average. In addition, the case studies also show that GCAN can produce reasonable explanations.

## introduction social media is indispensable in people's daily life, where users can express themselves, access news, and interact with each other. information can further spread through the social network. opinions and sentiments on source stories can be reflected by user participation and interaction. the convenient and low-cost essence of social networking brings collective intelligence, but at the same time leads to a negative by-product, the propagation of misinformation such as fake news. fake news is a kind of news story possessing intentionally false information on social media @xcite . the widespread of fake news can mislead the public, and produce unjust political, economic, or psychological profit for some parties @xcite . data mining and machine learning techniques were utilized to detect fake news @xcite . typical approaches rely on the content of new articles to extract textual features, such as n-gram and bag of words, and apply supervised learning (e.g., random forest and support vector machine) for binary classification @xcite . nlp researchers also learn advanced linguistic features, such as factive/assertive verbs and subjectivity @xcite and writing styles and consistency @xcite . multi-modal context information is also investigated, such as user profiles @xcite and retweet propagation @xcite . nevertheless, there are still critical challenges in detecting fake news online. first, existing contentbased approaches @xcite @xcite ) require documents to be long text, e.g., news articles, so that the representation of words and sentences can be better learned. however, tweets on social media are usually short text @xcite , which produces severe data sparsity problem. second, some stateof-the-art models @xcite @xcite require a rich collection of user comments for every news story, to learn the opinions of retweeters, which usually provide strong evidences in identifying fake news. however, most users on social media tend to simply reshare the source story without leaving any comments @xcite . third, some studies @xcite consider that the pathways of information cascade (i.e., retweets) in the social network are useful for classifying misinformation, and thus learn the representations of the tree-based propagation structures. however, it is costly to obtain the diffusion structure of retweets at most times due to privacy concerns @xcite . many users choose to hide or delete the records of social interactions. fourth, if the service providers or the government agencies desire to inspect who are the suspicious users who support the fake news, and which topics do they concern in producing fake news @xcite , existing models cannot provide explanations. although defend @xcite can generate reasonable explanation, it requires both long text of source articles and text of user comments. this paper deals with fake news detection under a more realistic scenario on social media. we predict whether a source tweet story is fake, given only its short text content and its retweet sequence of users, along with user profiles. that said, we detect fake news under three settings: (a) short-text source tweet, (b) no text of user comments, and (c) no network structures of social network and diffusion network. moreover, we require the fake news detection model to be capable of explainability, i.e., highlighting the evidence when determining a story is fake. the model is expected to point out the suspicious retweeters who support the spreading of fake news, and highlight the words they especially pay attention to from the source tweet. to achieve the goal, we propose a novel model, graph-aware co-attention network (gcan) 1 . we first extract user features from their profiles and social interactions, and learn word embeddings from the source short text. then we use convolutional and recurrent neural networks to learn the representation of retweet propagation based on user features. a graph is constructed to model the potential interactions between users, and the graph convolution network is used to learn the graph-aware representation of user interactions. we develop a dual co-attention mechanism to learn the correlation between the source tweet and retweet propagation, and the co-influence between the source tweet and user interaction. the binary prediction is generated based on the learned embeddings. we summarize the contributions as follows. we organize this paper as follows. section 2 reviews the relevant approaches to fake news detection in social media. we describe the problem statement in section 3. then in section 4, the details of our proposed gcan model will be elaborated. section 5 demonstrates the evaluation settings and results. we conclude this work in section 6. ## related work content-based approaches rely on the text content to detect the truthfulness of news articles, which usually refer to long text. a variety of text characteristics are investigated for supervised learning, including tf-idf and topic features @xcite , language styles (e.g., part of speech, factive/assertive verbs, and subjectivity) @xcite , writing styles and consistency @xcite , and social emotions @xcite . @xcite find the enquiry phrases from user responses are useful, and @xcite use recurrent neural networks to learn better representations of user responses. user-based approaches model the traits of users who retweet the source story. @xcite extract account-based features, such as "is verified", gender, hometown, and number of followers. @xcite unveil user profiles between fake and real news are significantly different. crnn @xcite devise a joint recurrent and convolutional network model (crnn) to better represent retweeter's profiles. session-based heterogeneous graph embedding @xcite is proposed to learn the traits of users so that they can be identified in shared accounts. however, since such a method relies on session information, it cannot be directly applied for fake news detection. structure-based approaches leverage the propagation structure in the social network to detect fake news. @xcite leverage the implicit information, i.e., hashtags and urls, to connect conversations whose users do not have social links, and find such implicit info can improve the performance of rumor classification. @xcite create a kernel-based method that captures high-order patterns differentiating different types of rumors. @xcite develop a tree-structured recursive neural networks to learn the embedding of rumor propagation structure. although multi-relational graph embedding methods @xcite are able to effectively learn how different types of entities (related to source news articles) interact with each other in a heterogeneous information network for classification tasks, they cannot be applied for the inductive setting, i.e., detecting the truthfulness of new-coming tweets. hybrid-based approaches consider and fuse multi-modal context information regarding the source tweets. csi @xcite learns the sequential retweet features by incorporating response text and user profiles, and generates suspicious scores of users based on their social interactions. @xcite develop an event adversarial neural network to learn transferable features by removing the event-specific features, along with convolutional neural networks to extract textual and visual features. defend @xcite jointly learns the sequential effect of response comments and the correlation between news content and comments, and use an attention mechanism to provide explainability. we compare our work and the most relevant studies in ## problem statement let ψ = {s 1 , s 2 ...s |ψ| } be a set of tweet stories, and 𝐅: product 𝐇 𝑔 : sum 𝐚 𝑔 : softmax ො 𝐠: product 𝐅 t : product 𝐇 𝑠 : sum 𝐚 𝑠 : softmax ො 𝐬1: product 𝐟: concatenate fc layer ො 𝐲: prediction 𝐠 1 𝐠 2 𝐠 𝑛 ... 𝐅 t : product 𝐇 𝑠 : sum 𝐚 𝑠 : softmax ො 𝐬2: product 𝐅: product 𝐇 𝑐 : sum 𝐚 𝑐 : softmax ƹ 𝐜: product 𝐬 1 𝐬 2 𝐬 𝑚 ... 𝐜 1 𝐜 2 𝐜 𝜋 ... 𝒉 1 𝒉 2 𝒉 𝒏 ... 𝒗1 𝑣2 𝑣𝑚 gru gru gru ... 𝐱1 𝐱2 𝐱𝑛 cnn cnn cnn ... 𝐞1 𝐞2 𝐞𝑚 𝐱1 𝐱2 𝐱𝑛 ... ... ... 𝐱1 𝐱2 𝐱𝑛 gru gru gru ... ... ... 𝐱1 𝐞1 source tweet 𝐞2 𝐞3 𝐞𝑚 ... 𝐱2 𝐱3 𝐱4 𝐱𝑛 ... ... pooling retweet order source-interaction co-attention source-propagation co-attention gcn gcn gcn graph-aware representation source tweet encoding cnn-based propagation representation gru-based propagation representation who retweets story s i , and @xmath0, 2, ..., k (i.e., @xmath1. we denote the set of users who retweet story s i as u i . in r i , we denote the user who originally shares s i as u 1 at time t 1 . for @xmath2, user u j retweets s i at t j (t @xmath3. each story s i is associated with a binary label y @xmath4, 1} to represent its truthfulness, where y @xmath5, and y @xmath6. given a source tweet s i , along with the corresponding propagation path r i containing users u j who retweet s i as well as their feature vectors x j , our goal is to predict the truthfulness y i of story s i , i.e., binary classification. in addition, we require our model to highlight few users u @xmath7. ## the proposed gcan model we develop a novel model, graph-aware co-attention networks (gcan), to predict fake news based on the source tweet and its propagation-based users. gcan consists of five components. the first is user characteristics extraction: creating features to quantify how a user participates in online social networking. the second is new story encoding: generating the representation of words in the source tweet. the third is user propagation representation: modeling and representing how the source tweet propagates by users using their extracted characteristics. the fourth is dual co-attention mechanisms: capturing the correlation between the source tweet and users' interactions/propagation. the last is making prediction: generating the detection outcome by concatenating all learned representations. ## experiments we conduct experiments to answer three questions: ## conclusion in this study, we propose a novel fake news detection method, graph-aware co-attention networks (gcan). gcan is able to predict whether a short-text tweet is fake, given the sequence of its retweeters. the problem scenario is more realistic and challenging than existing studies. evaluation results show the powerful effectiveness and the reasonable explainability of gcan. besides, gcan can also provide early detection of fake news with satisfying performance. we believe gcan can be used for not only fake news detection, but also other short-text classification tasks on social media, such as sentiment detection, hate speech detection, and tweet popularity prediction. we will explore model generalization in the future work. besides, while fake news usually targets at some events, we will also extend gcan to study how to remove eventspecific features to further boost the performance and explainability.