Four Years of Fake News

I published a new pre-print where I briefly review the scientific literature on fake news published up to 2020. You can read it here: Four Years of Fake News. A Quantitative Analysis of the Scientific Literature

Abstract

Introduction: Since 2016, “fake news” has been the main buzzword for online misinformation and disinformation. This term has been widely used and discussed by scholars, leading to hundreds of publications in a few years. This report provides a quantitative analysis of the scientific literature on the topic published up to 2020.

Methods: Documents mentioning the keyword “fake news” have been searched in Scopus, a large multidisciplinary scientific database. Frequency analysis of metadata and automated lexical analysis of titles and abstracts have been employed to answer the research questions.

Results: 2,368 scientific documents mentioned “fake news” in the title or abstract, published by 5,060 authors and 1,225 sources. Until 2016 the number of documents mentioning the term was less than 10 per year, suddenly rising from 2017 (203 documents), and steadily increasing in the following years (477 in 2018, 694 in 2019, and 951 in 2020). Among the most prolific countries are the USA and European countries such as the UK, but also many non-Western countries such as India and China. Computer Science and Social Sciences are the disciplinary fields with the largest number of documents published. Three main thematic areas emerged: computational methodologies for fake news detection, the social and individual dimension of fake news, and fake news in the public and political sphere. There are 10 documents with more than 200 citations, and two papers with a record number of citations (Alcott & Gentzkow, 2017; Lazer et al., 2018).

Conclusions: Research on “fake news” keeps on the rise, with a marked upward trend following the 2016 USA Presidential election. Despite having been the subject of debate and also criticism, the term is still widely used. A strong methodological interest in fake news detection through machine learning algorithms emerged, which – it can be argued – can be profitably balanced by a social science approach able to unpack the phenomenon also from a qualitative and theoretical point of view. Although dominated by the USA and other Western countries, the research landscape includes different countries of the world, thus enabling a wider and more nuanced knowledge of the problem. A constantly growing field of study like the one concerning fake news requires scholars to have a general overview of the scientific productions on the topic, and systematic literature reviews can be of help. The variety of perspectives and topics addressed by scholars also means that future analyses will need to focus on more specific topics.