Early Detection of Fake News by Utilizing the Credibility of News, Publishers, and Users based on Weakly Supervised Learning (2020)

Abstract: The dissemination of fake news significantly affects personal reputation and public trust. Recently, fake news detection has attracted tremendous attention, and previous studies mainly focused on finding clues from news content or diffusion path. However, the required features of previous models are often unavailable or insufficient in early detection scenarios, resulting in poor performance. Thus, early fake news detection remains a tough challenge. Intuitively, the news from trusted and authoritative sources or shared by many users with a good reputation is more reliable than other news. Using the credibility of publishers and users as prior weakly supervised information, we can quickly locate fake news in massive news and detect them in the early stages of dissemination. In this paper, we propose a novel structure-aware multi-head attention network (SMAN), which combines the news content, publishing, and reposting relations of publishers and users, to jointly optimize the fake news detection and credibility prediction tasks. In this way, we can explicitly exploit the credibility of publishers and users for early fake news detection. We conducted experiments on three real-world datasets, and the results show that SMAN can detect fake news in 4 hours with an accuracy of over 91%, which is much faster than the state-of-the-art models.

## introduction the widespread dissemination of fake news has lead to a significant influence on personal fame, public trust, and security. for example, spreading misinformation, such as "asians are more vulnerable to novel coronavirus" 1 about covid-19 has very serious repercussions, making people ignore the harmfulness of the virus and directly affecting public health. research has shown that misinformation spreads faster, farther, deeper, and more widely than true information @xcite . therefore, fake news detection on social media has attracted tremendous attention recently in both research and industrial fields. early research on fake news detection mainly focused on the design of effective features from various sources, including textual content, user profiling data, and news diffusion patterns. linguistic features, such as writing styles and sensational headlines @xcite , lexical and syntactic analysis @xcite , have been explored to separate fake news from true news. apart from linguistic features, some studies also proposed a series of user-based features @xcite , and temporal features @xcite about the news diffusion. however, these feature-based methods are very time-consuming, biased, and require a lot of labor to design. besides, these features are easily manipulated by users. to solve the above problems, many recent studies @xcite @xcite @xcite apply various neural networks to automatically learn high-level representations for fake news detection. for example, recurrent neural network (rnn) @xcite , convolutional neural network (cnn) @xcite , matrix factorization @xcite and graph neural network @xcite are applied to learn the representation of content and diffusion graph of news. these methods only apply more types of information for fake news detection, but paying little attention to early detection. moreover, these models can only detect fake news in consideration of all or a fixed proportion of repost information, while in practice they cannot detect fake news in the early stage of news propagation @xcite . some studies @xcite @xcite explore to detect fake news early by relying on a minimum number of posts. the main limitation of these methods is that they ignore the importance of publishers' and users' credibility for the early detection of fake news. when we humans see a piece of breaking news, we firstly may use common sense to judge whether there are factual errors in it. at the same time, we will also consider the reputation of the publishers and reposted users. people tend to believe the news from a trusted and authoritative source or the news shared by lots of users with a good reputation. if the publisher is reliable, we tend to believe this news. on the other hand, if the news is reposted by many low-reputation users in a short period, it may be that some spammers tried to heat up on the news @xcite , resulting in lower credibility of the news. inspired by the above observation, we explicitly take the credibility of publishers and users as supervised information, and model fake news detection as a multi-task classification task. we can annotate a small part of publishers and users by their historical publishing and reposting behaviors. although the credibility of publishers and users does not always provide correct information, they are necessary complementary supervised information for fake news detection. to make the credibility information generalized to other unannotated users, we construct a heterogeneous graph to build the connections of publishers, news, and users. through a graph-based encoding algorithm, every node in the graph will be influenced by the credibility of publishers and users. in this paper, we address the following challenges: (1) how to fully encode the heterogeneous graph structure and news content; and (2) how to explicitly utilize the credibility of publishers and users for facilitating early detection of fake news. to tackle the above challenges, we propose a novel structureaware multi-head attention network for early detection of fake news. firstly, we design a structure-aware multi-head attention module to learn the structure of the publishing graph and produce the publisher representations for the credibility prediction of publishers. then, we apply the structure-aware multi-head attention module to encode the diffusion graph of the news among users and generate user representations for the credibility prediction of users. finally, we apply a convolutional neural network to map the news text from word embedding to semantic space and utilize the fusion attention module to combine the news, publisher, and user representations for early fake news detection. the contributions of this paper can be summarized as follows: ## problem formulation let @xmath0, m 2 , . . . m |n | be the set of news. each news m j has one publisher at least and k users {r 1 , r 2 , . . . , r k } to repost it at most. the publisher-news relations form a publishing graph g(v p , e). the publisher-user relations form a diffusion graph g(v u , e). in the diffusion graph of news, we regard users who repost the news as the neighbor nodes of the publisher. we use |p |, |n |, and |u | to denote the amount of publishers, news, and users respectively. for fake news detection task, our target is to learn a function p(c|m j , p, n , u; θ 3 ) to predict whether a piece of news is fake or not. c is class label of the news and θ 3 represents all parameters of the model. in this paper, we design a credibility prediction subtask to explicitly utilize the users' or publishers' credibility information for fake news detection. for credibility prediction task, our goal is to learn a function p(c|g(v p , e), p; θ 1 ) or p(c|g(v u , e), u; θ 2 ) to predict the credit scores of publishers or users by publishing graph or diffusion graph. ## the proposed framework the proposed framework consists of three major components: (1) publisher credibility prediction; (2) user credibility prediction; and (3) fake news classification. figure 1 illustrates the architecture of the proposed model. ## experiments in this section, we introduce the experiments to evaluate the effectiveness of sman. specifically, we aim to answer the following evaluation questions: ## conclusion this paper proposes a novel structure-aware multi-attention network, which combines news content, the heterogeneous graphs among publishers and users, and jointly optimizes the task of false news detection and user credibility prediction for early fake news detection. different from most existing research extracting hand-crafted features or deep learning methods, we explicitly treat the credibility of publishers and users as a kind of weakly supervised information for facilitating fake news detection. extensive experiments conducted on three real-world datasets show that the proposed model can significantly surpass other state-of-the-art models on both fake news classification and early detection task.