Causes and consequences of mainstream media dissemination of fake news: literature review and synthesis

Tsfati, Yariv; Boomgaarden, Hajo G.; Strömbäck, Jesper; Vliegenthart, Rens; Damstra, Alyt; Lindgren, Elina

doi:10.1080/23808985.2020.1759443

Cited by 204 publications

(137 citation statements)

References 56 publications

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“…Furthermore, fake news can influence the media agenda to a certain extent (Vargo et al, 2018). Some scholars stress that the number of people consuming fake news does not correspond to the number of people that are aware of this information, which circumstantially suggests that they learn about major fake stories from mainstream media (Tsfati et al, 2020).…”

Section: Discussionmentioning

confidence: 99%

How Fake News Spreads Online?

2020

International Journal of Media and Information Literacy

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Permeating the Internet and reaching millions of users, fake news became a pervasive phenomenon on the public agenda since the US Presidential election in 2016. The term, despite its ambiguity, has been widely used in research to describe false messages created with the intent to mislead. However empirical evidence on how fake news spreads and circulates between individuals and different social groups remains limited.For this literature review, I collected 21 academic articles published in English between 2016 and 2020 that explore the audience of fake news, patterns of its dissemination, and the role played by average users and bots in this process. The reviewed studies sometimes arrive at contradictory conclusions regarding important aspects of the focal phenomenon: for example, authors differently evaluate the scale of it and reach opposing conclusions studying the influence of bots on the fake news spread. Moreover, I observe certain contextual imbalances: many papers focus on the US political agenda and collect data from Facebook or Twitter, all but neglecting other digital platforms where disinformation can circulate.In this paper, I argue that future research needs a systematic exploration of users' motivations to share fake news and more precise look into the role of media in misinformation dissemination. It is also important to compare the spread of fake news within different political contexts and media systems to explore how local peculiarities affect its circulation.

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Section: Discussionmentioning

confidence: 99%

How Fake News Spreads Online?

2020

International Journal of Media and Information Literacy

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“…Outra problemática associada ao conceito de desinformação é que nos usos do conceito frequentemente há uma busca por produzir uma dualidade entre detentores da verdade (como o jornalismo) e atores que buscam gerar desestabilidade, porém esta dualidade é limitada já que o contexto da circulação da desinformação é bem mais complexo (OLIVEIRA, 2020a). Além disso, atores como a própria imprensa podem colaborar para o espalhamento da desinformação (TSFATI et al 2020). Neste sentido, reconhecemos as limitações do conceito, mas utilizamos desinformação para designar o conteúdo que analisamos como forma de delimitação analítica do fenômeno que estamos observando.…”

Section: Desinformação No Whatsappunclassified

Desinformação sobre o Covid-19 no WhatsApp: a pandemia enquadrada como debate político

Soares

Recuero

Volcan³

et al. 2021

Ci. Inf. Rev.

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<p>Neste artigo, discutimos como é enquadrada a desinformação sobre o Covid-19 no WhatsApp no Brasil. Nossos objetivos são: (1) analisar a influência do discurso político nestes processos e (2) identificar as características da desinformação sobre o Covid-19 nesta ferramenta. Para isso, utilizamos a análise de conteúdo em um conjunto de 802 mensagens coletadas por meio do Monitor do WhatsApp em março e abril de 2020. Os nossos principais resultados mostram que: (1) a pandemia foi enquadrada como debate político e a desinformação foi utilizada para fortalecer uma narrativa pró-Bolsonaro em momento de crise do governo; e (2) há grande prevalência de teorias da conspiração nas mensagens analisadas e a opinião aparece como a principal estratégia discursiva, resultado que pode estar relacionado com as características da plataforma.</p>

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“…For example, many fake news websites in the USA, like WashingtonPost.com.co, USAToday.com.co, etc. were identified (Tsfati et al , 2020). Such channels have shown a spike in similar phenomena found throughout the world, ranging from India [8] to France (French Social Media Awash with Fake News Stories from Sources ‘Exposed to Russian Influence’ Ahead of Presidential Election | The Independent | The Independent, n.d.), Israel (Phone Hacking?…”

Section: Introductionmentioning

confidence: 99%

A systematic survey on deep learning and machine learning approaches of fake news detection in the pre- and post-COVID-19 pandemic

Varma

Verma

Vijayvargiya

et al. 2021

IJICC

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PurposeThe rapid advancement of technology in online communication and fingertip access to the Internet has resulted in the expedited dissemination of fake news to engage a global audience at a low cost by news channels, freelance reporters and websites. Amid the coronavirus disease 2019 (COVID-19) pandemic, individuals are inflicted with these false and potentially harmful claims and stories, which may harm the vaccination process. Psychological studies reveal that the human ability to detect deception is only slightly better than chance; therefore, there is a growing need for serious consideration for developing automated strategies to combat fake news that traverses these platforms at an alarming rate. This paper systematically reviews the existing fake news detection technologies by exploring various machine learning and deep learning techniques pre- and post-pandemic, which has never been done before to the best of the authors’ knowledge.Design/methodology/approachThe detailed literature review on fake news detection is divided into three major parts. The authors searched papers no later than 2017 on fake news detection approaches on deep learning and machine learning. The papers were initially searched through the Google scholar platform, and they have been scrutinized for quality. The authors kept “Scopus” and “Web of Science” as quality indexing parameters. All research gaps and available databases, data pre-processing, feature extraction techniques and evaluation methods for current fake news detection technologies have been explored, illustrating them using tables, charts and trees.FindingsThe paper is dissected into two approaches, namely machine learning and deep learning, to present a better understanding and a clear objective. Next, the authors present a viewpoint on which approach is better and future research trends, issues and challenges for researchers, given the relevance and urgency of a detailed and thorough analysis of existing models. This paper also delves into fake new detection during COVID-19, and it can be inferred that research and modeling are shifting toward the use of ensemble approaches.Originality/valueThe study also identifies several novel automated web-based approaches used by researchers to assess the validity of pandemic news that have proven to be successful, although currently reported accuracy has not yet reached consistent levels in the real world.

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Causes and consequences of mainstream media dissemination of fake news: literature review and synthesis

Cited by 204 publications

References 56 publications

How Fake News Spreads Online?

How Fake News Spreads Online?

Desinformação sobre o Covid-19 no WhatsApp: a pandemia enquadrada como debate político

A systematic survey on deep learning and machine learning approaches of fake news detection in the pre- and post-COVID-19 pandemic

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