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.
Highlights
The recent debate on COVID-19 vaccination, political debates that took place in national level the last two decades, other events and issues of global interest, such as world tragedies, war-related migration, global warming, etc., raise discussions in social media and online news. Social media had their own special role in some of these events, starting from the role of Twitter in the US Presidential Elections of 2008 until the more recent Elections of 2016 [1] that popularized the use of the term “fake news” around the world and made it the Word of the Year in the Collins Dictionary in 2017 [2]
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
The different Graph Convolutional Networks (GCNs) techniques presented in this article demonstrate the advantages of graph-based representations and GCNs over simple CNNs or Recursive Neural Networks (RNNs) in graph and node classification tasks
Summary
The recent debate on COVID-19 vaccination, political debates that took place in national level the last two decades, other events and issues of global interest, such as world tragedies, war-related migration, global warming, etc., raise discussions in social media and online news. Social media had their own special role in some of these events, starting from the role of Twitter in the US Presidential Elections of 2008 until the more recent Elections of 2016 [1] that popularized the use of the term “fake news” around the world and made it the Word of the Year in the Collins Dictionary in 2017 [2]. The COVID-19 pandemic and the lack of information regarding the reasons, prevention or cure, especially during the first months of the pandemic, fueled the spread of numerous rumors and hoaxes and led to several organized attempts to spread misinformation [3]
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