The spreading of misinformation online

By Michela Del Vicario, Alessandro Bessi, Fabiana Zollo, Fabio Petroni, Antonio Scala, Guido Caldarelli, H. Eugene Stanley, Walter Quattrociocchi. Proceedings of the National Academy of Sciences of the United States of America, 2016

2,089 citations recorded by the source corpus.

Subfields: Sociology and Political Science, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/c44cd3b6864293e4449ce78191c54bf71313d544

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Citations in the corpus

Correction Experiences on Social Media During COVID-19 (2021, background): “…misinformation are by no means new, with multiple studies raising alarms about the prevalence of misinformation across social media platforms for emerging disease and enduring health issues (e.g., Broniatowski et al., 2018; Chou et al., 2018; Del Vicario et al., 2016; Guidry et al., 2015).”
Systematic Literature Review on the Spread of Health-related Misinformation on Social Media (2019, background): “Other relevant studies have focused on important concepts such as misperception and confirmation bias, whereby people's views on factual matters are strongly influenced by prior beliefs (Taber and Lodge, 2006; Nyhan and Reifler, 2010; Jerit and Barabas, 2012) ; polarization within networks (Lewandow”
Swedish teenagers’ difficulties and abilities to determine digital news credibility (2019, background): “New media and modern journalism can, in many ways, facilitate the spread of exaggerations and lies (Del Vicario et al., 2016; Silverman, 2015).”
Can’t We Just Disregard Fake News? The Consequences of Exposure to Inaccurate Information (2018, background): “But deciding what to evaluate and how to evaluate proves challenging, particularly difficult when accusations of fake news and bias muddy contemporary discourse (e.g., Del Vicario et al., 2016; Lazer et al., 2018; Rapp & Braasch, 2014; Vosoughi, Roy, & Aral, 2018).”
Confirmation Bias and the Persistence of Misinformation on Climate Change (2021, methodology): “Data-driven models with massive social media data also suggest that confirmation bias is a key determinant for misinformation spreading and attitude polarization (e.g., Del Vicario et al., 2016).”
Mistrust and misinformation: A two-component, socio-epistemic model of belief in conspiracy theories (2020, background): “…processing contributes to BCT therefore requires an evolving appreciation of digital forces such as “echo chambers” and “filter bubbles” (Bakshy, Messing, & Adamic, 2015; Del Vicario et al., 2016; Flaxman et al., 2016) that can result in a kind of “confirmation bias on steroids” (Pierre, 2019).”
Emotional contagion in organizational life (2018, background): “While research in this domain is still relatively nascent, extant work suggests that emotions can in fact spread virtually, both among teams and, more broadly, across entire social networking platforms (Cheshin et al., 2011; Del Vicario et al., 2016; Ferrara & Yang, 2015; Kramer et al., 2014).”
The growing ubiquity of algorithms in society: implications, impacts and innovations (2018, background): “Bastos [11], also in this issue, discusses other aspects of the human–algorithmic interface, some of which have also been described in the analysis of political polarisation [104,105].”
Shared identity and shared information in social media: development and validation of the identity bubble reinforcement scale (2018, background): “It has been well documented in previous studies that online interactions tend to center around social cliques or bubbles that are manifested in shared-group ideologies (e.g., political factions, world views) with a strong reliance on similar-minded social networks as sources of reliable information ”