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The history of journalism and news diffusion is tightly coupled with the effort to dispel hoaxes, misinformation, propaganda, unverified rumours, poor reporting, and messages containing hate and divisions. With the explosive growth of online social media and billions of individuals engaged with consuming, creating, and sharing news, this ancient problem has surfaced with a renewed intensity threatening our democracies, public health, and news outlets credibility. This has triggered many researchers to develop new methods for studying, understanding, detecting, and preventing fake-news diffusion; as a consequence, thousands of scientific papers have been published in a relatively short period, making researchers of different disciplines to struggle in search of open problems and most relevant trends. The aim of this survey is threefold: first, we want to provide the researchers interested in this multidisciplinary and challenging area with a network-based analysis of the existing literature to assist them with a visual exploration of papers that can be of interest; second, we present a selection of the main results achieved so far adopting the network as an unifying framework to represent and make sense of data, to model diffusion processes, and to evaluate different debunking strategies. Finally, we present an outline of the most relevant research trends focusing on the moving target of fake-news, bots, and trolls identification by means of data mining and text technologies; despite scholars working on computational linguistics and networks traditionally belong to different scientific communities, we expect that forthcoming computational approaches to prevent fake news from polluting the social media must be developed using hybrid and up-to-date methodologies.
Recent years have witnessed remarkable progress towards computational fake news detection. To mitigate its negative impact, we argue that it is critical to understand what user attributes potentially cause users to share fake news. The key to this ca
The rise of fake news in the past decade has brought with it a host of consequences, from swaying opinions on elections to generating uncertainty during a pandemic. A majority of methods developed to combat disinformation either focus on fake news co
Although significant effort has been applied to fact-checking, the prevalence of fake news over social media, which has profound impact on justice, public trust and our society, remains a serious problem. In this work, we focus on propagation-based f
Todays social media platforms enable to spread both authentic and fake news very quickly. Some approaches have been proposed to automatically detect such fake news based on their content, but it is difficult to agree on universal criteria of authenti
The wide spread of fake news in social networks is posing threats to social stability, economic development and political democracy etc. Numerous studies have explored the effective detection approaches of online fake news, while few works study the