# A Survey on the Use of Graph Convolutional Networks for Combating Fake News
###### Abstract  
The combat against fake news and disinformation is an ongoing, multi-faceted task for researchers in social media and social networks domains, which comprises not only the detection of false facts in published content but also the detection of accountability mechanisms that keep a record of the trustfulness of sources that generate news and, lately, of the networks that deliberately distribute fake information. In the direction of detecting and handling organized disinformation networks, major social media and social networking sites are currently developing strategies and mechanisms to block such attempts. The role of machine learning techniques, especially neural networks, is crucial in this task. The current work focuses on the popular and promising graph representation techniques and performs a survey of the works that employ Graph Convolutional Networks (GCNs) to the task of detecting fake news, fake accounts and rumors that spread in social networks. It also highlights the available benchmark datasets employed in current research for validating the performance of the proposed methods. This work is a comprehensive survey of the use of GCNs in the combat against fake news and aims to be an ideal starting point for future researchers in the field. fake news; graph convolutional networks (GCNs); misinformation; fake accounts; bots; astroturing +
Footnote †: journal: _Future Internet_  
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that flood up the news sites but primarily social media. These sites monitor a large amount of news sources, including popular social media accounts of politicians, artists, bloggers and other public persons, and have devised their truthfulness rating systems, such as the Truth-O-Meter of PolitiFact, which assigns a score to each public statement and are useful resources for checking the facts of individual posts or the overall credibility of public persons. In the same discipline are the sites that detect and debunk hoaxes, such as the Hellenic Hoaxes ([[https://www.ellinikahaoxes.gr/](https://www.ellinikahaoxes.gr/)]([https://www.ellinikahaoxes.gr/](https://www.ellinikahaoxes.gr/)), accessed on 17 February 2022) in Greece, Cek-Fakta ([[https://cekfakta.com/](https://cekfakta.com/)]([https://cekfakta.com/](https://cekfakta.com/)), accessed on 17 February 2022) in Indonesia and others, who mostly focus on rumors and stories that lurk on social media and intentionally attempt to misinform readers. Human editors are collecting facts that debunk the myths of social media and reveal the true story behind the fake facts. The International Fact-Checking Network ([[https://www.poynter.org/ifcn/](https://www.poynter.org/ifcn/)]([https://www.poynter.org/ifcn/](https://www.poynter.org/ifcn/)), accessed on 17 February 2022) is a growing community of fact-checkers around the world that collects factual information and facilitates the networking, capacity building and collaboration of fact-checkers in order to combat against fake facts and news. Hoaxy ([[https://hoaxy.osome.iu.edu/](https://hoaxy.osome.iu.edu/)]([https://hoaxy.osome.iu.edu/](https://hoaxy.osome.iu.edu/)), accessed on 17 February 2022) is another popular tool in this domain, which mostly focuses on the visualization of how news (including fake news and hoaxes) flows within social media. Hoaxy tracks the social sharing of links to stories published by two types of websites: (i) low-credibility sources that often publish inaccurate, unverified claims and (ii) independent fact-checking organizations, such as snopes, politifact and factcheck, that routinely fact check unverified claims.