# A Survey on the Use of Graph Convolutional Networks for Combating Fake News
###### Abstract
In the second direction, tools such as NewsGuard ([[https://www.newsguardtech.com/](https://www.newsguardtech.com/)]([https://www.newsguardtech.com/](https://www.newsguardtech.com/)), accessed on 17 February 2022) allow news consumers to navigate through reliable and unreliable news sources online, harnessing the power of a large crowd of journalists and reporters, who manually review news sources based on different criteria, including the false content rate, responsibility in correcting errors, use of deceptive headlines (furthermore, known as clickbait), ownership and financing disclosure, etc., which assess basic practices of credibility and transparency. The respective news sources are categorized as: (i) green when they adhere to the basic standards of credibility and transparency, (ii) red when the fail to meet these standards and severely violate journalistic standards, (iii) satire when they are not actual news sites but clearly publish false information for satire purposes, or (iv) platform when the content is user-generated and thus has to be cross-checked per case for its reliability. Another category of tools that capitalizes on the detection of fake sources includes Botomoter ([[https://botometer.osome.iu.edu/](https://botometer.osome.iu.edu/)]([https://botometer.osome.iu.edu/](https://botometer.osome.iu.edu/)), accessed on 17 February 2022), a site that checks the activity of a Twitter account and gives it a score that defines its bot-like activity. In the direction of detecting and handling organized disinformation networks, also called astroturfing campaigns [4], major social media and social networking sites are  
Figure 1: An overview of the various tools that combat fake news and misinformation. currently developing strategies and mechanisms to block such attempts. For example, Facebook recently announced that it took down disinformation networks tied to major political actors and events around the world (e.g., militant groups, groups that intensify the immigration crisis, anti-vaccine groups etc.) ([[https://www.washingtonpost.com/technology/2021/12/01/facebook-disinformation-report/](https://www.washingtonpost.com/technology/2021/12/01/facebook-disinformation-report/)]([https://www.washingtonpost.com/technology/2021/12/01/facebook-disinformation-report/](https://www.washingtonpost.com/technology/2021/12/01/facebook-disinformation-report/)), accessed on 17 February 2022). However, the task of uncovering organized fake news campaigns is still a hard one that requires the proper understanding of the social landscape [5] and the internal mechanisms of social media and networking platforms and the ways news spread on them [6]. In this paper, we focus on the above tasks, the detection and evaluation of fake news sources, fake accounts and organized disinformation attempts that generate and spread fake news in social media. We mainly examine the network structure and the network-related features of this spreading of news and emphasize the use of graph neural networks in the modeling of similar tasks. Although there already exist a few survey works on GCNs and their use in classification tasks [7; 8] and several works that tackle the fake news problem using deep neural networks [9; 10; 11; 12], this is the first work, according to our knowledge, that specifically surveys the use of Graph Convolutional Networks for the detection of fake news, fake users and rumors. In the sections that follow, we perform a survey on the methods that use Graph Convolutional Networks to model the information related to fake news (i.e., content and social graph) and detect fake news items, rumors, bot and spammer accounts. Section 2 introduces the main concepts of Graph Convolutional Networks and briefly explains how graphs are constructed. Section 3 details the three different research directions that focus on detecting fake news items, sources and rumors or misinformation campaigns and discusses the main research works in each direction.