Science vs Conspiracy: Collective Narratives in the Age of Misinformation

By Alessandro Bessi, Mauro Coletto, G. Davidescu, Antonio Scala, Guido Caldarelli, Walter Quattrociocchi. PLoS ONE, 2014

564 citations recorded by the source corpus.

The large availability of user provided contents on online social media facilitates people aggregation around shared beliefs, interests, worldviews and narratives. In spite of the enthusiastic rhetoric about the so called collective intelligence unsubstantiated rumors and conspiracy theories—e.g., chemtrails, reptilians or the Illuminati—are pervasive in online social networks (OSN). In this work we study, on a sample of 1.2 million of individuals, how information related to very distinct narratives—i.e. main stream scientific and conspiracy news—are consumed and shape communities on Facebook. Our results show that polarized communities emerge around distinct types of contents and usual consumers of conspiracy news result to be more focused and self-contained on their specific contents. To test potential biases induced by the continued exposure to unsubstantiated rumors on users’ content selection, we conclude our analysis measuring how users respond to 4,709 troll information—i.e. parodistic and sarcastic imitation of conspiracy theories. We find that 77.92% of likes and 80.86% of comments are from users usually interacting with conspiracy stories.

Subfields: Sociology and Political Science, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/9e2f737cd0e655bb9ba5d363ff909fe89f974d37

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Sociology and Political Science

In Statistical and Nonlinear Physics

Citations in the corpus

Debunking in a world of tribes (2015, methodology): “ a community driven approach where users can tag false contents. However, such practices are controversial since they raise issues regarding the free circulation of contents. Moreover, recent studies (8,9,25,26) have shown that social and cognitive factors, like conﬁrmation bias and social reinforce”
Attitudes Towards Science (2018, background): “For example, Bessi et al. (2015) found that antiscience conspiracy content was shared on Facebook roughly three times as often as science content.”
The echo chamber effect on social media (2021, background): “Selective exposure (19) and conﬁrmation bias (20) (i.e., the tendency to seek information adhering to preexisting opinions) may explain the emergence of echo chambers on social media (1, 17, 21, 22).”
Disinformation and social bot operations in the run up to the 2017 French presidential election (2017, background): “…created concerted efforts to game financial systems (Ferrara, 2015); conspiracy groups orchestrate campaigns to distribute fake scientific articles to support anti-vaccination and other anti-science movements, creating massive public health issues (Bessi, et al., 2015; Del Vicario, et al., 2016).”
Polarization of the Vaccination Debate on Facebook (2018, background): “Users can access a wide amount of information and narratives and selection criteria are biased toward personal viewpoints [14,15,16].”
Testing Logic-based and Humor-based Corrections for Science, Health, and Political Misinformation on Social Media (2019, background): “, 2018; Guidry, Carlyle, Messner, & Jin, 2015), and scientific topics (Bessi et al., 2015; Del Vicario et al., 2017).”
The spread of low-credibility content by social bots (2017, unknown): “Due to the impossibility to fact-check millions of articles, this approach of using sources as proxies for misinformation labels is increasingly adopted in the literature cited in the main text, and more [53, 49, 65, 3, 17].”
Mistrust and misinformation: A two-component, socio-epistemic model of belief in conspiracy theories (2020, background): “…theories are started or fueled by online “trolls” and “conspiracy theory entrepreneurs” who may have low degrees of actual belief conviction themselves (Bessi et al., 2015; Sunstein & Vermeule, 2009), but are motivated to “erode trust in facts and reality” (Lewandowsky, Ecker, & Cook, 2017, p.…”
Shared identity and shared information in social media: development and validation of the identity bubble reinforcement scale (2018, background): “…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 (Bessi et al., 2015; Bakshy et al., 2015; Boutyline & Willer, 2017; Del Vicario et al., 2016; Zollo et al., 2017).”
Political Fact-Checking on Twitter: When Do Corrections Have an Effect? (2018, background): “Analyses of large-scale information consumption patterns on social media also suggest that some people may seek out rumors and avoid sources that might correct them (Bessi et al., 2015; Mocanu, Rossi, Zhang, Karsai, & Quattrociocchi, 2015).”
The Truth is Out There: Investigating Conspiracy Theories in Text Generation (2021, background): “Previous conspiracy theory studies contain datasets that are either small in size (Oliver and Wood, 2014), contain non-English data (Bessi et al., 2015), or pertain to events occurring after the release of GPT-2 (Ahmed et al., 2020; Uscinski et al., 2020).”
The COVID-19 social media infodemic (2020, background): “Indeed, users online tend to acquire information adhering to their worldviews [15], to ignore dissenting information [16, 17] and to form polarized groups around shared narratives [18, 19].”
Overview of the Special Issue on Trust and Veracity of Information in Social Media (2016, background): “Researchers have identified numerous sources of untrusted content, and through them they found that several online communities interact with narratives stemming from conspiracy theories [Bessi et al. 2015].”
The spreading of misinformation online (2016, methodology): “Recent works (10 – 12) have shown that increasing the exposure of users to unsubstantiated rumors increases their tendency to be credulous.”