scholarly journals From Postfiction to Protofiction: Conspiracy Theory, Social Networks, and Confabulation

2011 ◽  
Vol 1 (1) ◽  
Author(s):  
Toby Bennett
Keyword(s):  
Social Networks ◽  
Conspiracy Theory

scholarly journals On the Classification of Fake Information During the Coronavirus Pandemic

2021 ◽  
pp. 124-131
Author(s):  
Marina Zyryanova
Keyword(s):  
Social Networks ◽  
Information Source ◽  
Conspiracy Theory ◽  
Russian Regions ◽  
False Information ◽  
Public Response ◽  
Mass Consciousness ◽  
Short Period ◽  
Large Audience

This article presents the classification of fakes on grounds of the information source that underlies the occurrence of false information. The study was perfomed on the coronavirus fakes that spread in Russian Federation in March 2020 during the beginning of the coronavirus pandemic in our country. For the analysis, only those fakes were taken, which the Administrations of the Russian regions promptly denied in their official accounts on social networks. Based on this, only those fakes that caused the greatest public response were selected for analysis. In this article, the following types of fakes are distinguished: folklore, symmetric, interpretive, additional, and conspiracy. Folklore fakes in various variations reproduce the same motives and are associated with well-established ideas and stereotypes in the mass consciousness. Symmetrical fakes partially or completely transfer true facts from one territory (country, region) to another. They can also transfer information from one person (structure) to another (s). Interpretative fakes are associated with the incorrect interpretation of events, information disseminated, or decisions made by the authorities by individual individuals. Additional fakes for a short period of time continue the theme of previously thrown disinformation. Conspiracy fakes are associated with conspiracy theory, characterized by stuffing on a wide territory and a large audience This classification is not exhaustive and can be supplemented as new fakes appear and are studied. Also, within the framework of this article, recommendations are given on how to refute a particular fake, depending on its belonging to a particular type.

scholarly journals Understanding and countering the spread of conspiracy theories in social networks: Evidence from epidemiological models of Twitter data

2021 ◽  
Author(s):  
Julian Kauk ◽  
Helene Kreysa ◽  
Stefan R. Schweinberger
Keyword(s):  
Social Networks ◽  
Early Stage ◽  
Scientific Evidence ◽  
Mobile Network ◽  
Early Response ◽  
Conspiracy Theory ◽  
Epidemiological Models ◽  
Conspiracy Theories ◽  
Twitter Data ◽  
Fact Checking

Conspiracy theories in social networks are considered to have adverse effects on individuals' compliance with public health measures in the context of a pandemic situation. A deeper understanding of how conspiracy theories propagate through social networks is critical for the development of countermeasures. The present work focuses on a novel approach to characterize the propagation of conspiracy theories through social networks by applying epidemiological models to Twitter data. A Twitter dataset was searched for tweets containing hashtags indicating belief in the ``5GCoronavirus'' conspiracy theory, which states that the COVID-19 pandemic is a result of, or enhanced by, the enrollment of the 5G mobile network. Despite the absence of any scientific evidence, the ``5GCoronavirus'' conspiracy theory propagated rapidly through Twitter, beginning at the end of January, followed by a peak at the beginning of April, and ceasing/disappearing approximately at the end of June 2020. An epidemic SIR (Susceptible-Infected-Removed) model was fitted to this time series with acceptable model fit, indicating parallels between the propagation of conspiracy theories in social networks and infectious diseases. Extended SIR models were used to simulate the effects that two specific countermeasures, fact-checking and tweet-deletion, could have had on the propagation of the conspiracy theory. Our simulations indicate that fact-checking is an effective mechanism in an early stage of conspiracy theory diffusion, while tweet-deletion shows only moderate efficacy but is less time-sensitive. More generally, an early response is critical to gain control over the spread of conspiracy theories through social networks. We conclude that an early response combined with strong fact-checking and a moderate level of deletion of problematic posts is a promising strategy to fight conspiracy theories in social networks. Results are discussed with respect to their theoretical validity and generalizability.

scholarly journals Understanding and countering the spread of conspiracy theories in social networks: Evidence from epidemiological models of Twitter data

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0256179
Author(s):  
Julian Kauk ◽  
Helene Kreysa ◽  
Stefan R. Schweinberger
Keyword(s):  
Social Networks ◽  
Early Stage ◽  
Scientific Evidence ◽  
Mobile Network ◽  
Early Response ◽  
Conspiracy Theory ◽  
Epidemiological Models ◽  
Conspiracy Theories ◽  
Twitter Data ◽  
Fact Checking

Conspiracy theories in social networks are considered to have adverse effects on individuals’ compliance with public health measures in the context of a pandemic situation. A deeper understanding of how conspiracy theories propagate through social networks is critical for the development of countermeasures. The present work focuses on a novel approach to characterize the propagation of conspiracy theories through social networks by applying epidemiological models to Twitter data. A Twitter dataset was searched for tweets containing hashtags indicating belief in the “5GCoronavirus” conspiracy theory, which states that the COVID-19 pandemic is a result of, or enhanced by, the enrollment of the 5G mobile network. Despite the absence of any scientific evidence, the “5GCoronavirus” conspiracy theory propagated rapidly through Twitter, beginning at the end of January, followed by a peak at the beginning of April, and ceasing/disappearing approximately at the end of June 2020. An epidemic SIR (Susceptible-Infected-Removed) model was fitted to this time series with acceptable model fit, indicating parallels between the propagation of conspiracy theories in social networks and infectious diseases. Extended SIR models were used to simulate the effects that two specific countermeasures, fact-checking and tweet-deletion, could have had on the propagation of the conspiracy theory. Our simulations indicate that fact-checking is an effective mechanism in an early stage of conspiracy theory diffusion, while tweet-deletion shows only moderate efficacy but is less time-sensitive. More generally, an early response is critical to gain control over the spread of conspiracy theories through social networks. We conclude that an early response combined with strong fact-checking and a moderate level of deletion of problematic posts is a promising strategy to fight conspiracy theories in social networks. Results are discussed with respect to their theoretical validity and generalizability.

scholarly journals FACT-CHECKING: AN IMPORTANT TOOL TO COMBAT FAKE NEWS ON HEALTH IN COVID-19 PANDEMIC

2021 ◽  
pp. 10
Author(s):  
Keyword(s):  
Social Networks ◽  
Conspiracy Theory ◽  
Fake News ◽  
The Public ◽  
Social Media Networks ◽  
The Social ◽  
Fact Checking ◽  
Quantitative Descriptive ◽  
City State ◽  
False News

Objective: To demonstrate the importance of Fact-Checking tools in combating health fake news in the COVID-19 pandemic. Methods: Quantitative descriptive study, conducted during the Sars-Cov-2 pandemic. Fake news were accounted and identified through the website chequeado.com, registered in the Agência Lupo and Aos Fatos checking platforms, belonging to the International Fact-Checking Network, an international understanding with recognized news verification methodologies. The registered news originated from the social media/networks Facebook, Whatsapp, Instagram, Twitter, and websites. They were later classified according to content in Conspiracy Theory, Prevention/Treatment/Cure, Authorities/Agency Measures, Situation of a city, state and country, Causes, Symptoms, Public Figure and False Context. Results: 529 fake news about coronavirus were obtained, of these 306 were from the Agência Lupo platform, and 223 from the Aos Fatos platform. A total of 99 (18.72%) fake news were about Conspiracy Theory 99 (18.72%) Authorities/Agency Measures and 98 (18.53%) False Context. As for the origin of fake news 382 (72.21%) were from Facebook and 67 (12.66%) from Whatsapp. Conclusion: The Fact-Checking tools in combating misinformation on social networks are important because they deny false news, unlikely allegations, and no justification related to the Covid-19 pandemic. These check sites alert social networks, policymakers, and the public to create measures that educate and protect the integrity and health of individuals and prevent them from falling victim to misinformation.

Multilayer Social Networks

2016 ◽  
Author(s):  
Mark E. Dickison ◽  
Matteo Magnani ◽  
Luca Rossi
Keyword(s):  
Social Networks ◽  
Multilayer Social Networks

Hostility and Perceptions of Support in Ambiguous Social Interactions

2006 ◽  
Vol 27 (2) ◽  
pp. 108-115 ◽  
Author(s):  
Ana-Maria Vranceanu ◽  
Linda C. Gallo ◽  
Laura M. Bogart
Keyword(s):  
Social Support ◽  
Social Networks ◽  
Information Processing ◽  
Social Interactions ◽  
Social Information Processing ◽  
Perceived Social Support ◽  
Social Information ◽  
Inverse Association ◽  
Processing Bias ◽  
Information Processing Bias

The present study investigated whether a social information processing bias contributes to the inverse association between trait hostility and perceived social support. A sample of 104 undergraduates (50 men) completed a measure of hostility and rated videotaped interactions in which a speaker disclosed a problem while a listener reacted ambiguously. Results showed that hostile persons rated listeners as less friendly and socially supportive across six conversations, although the nature of the hostility effect varied by sex, target rated, and manner in which support was assessed. Hostility and target interactively impacted ratings of support and affiliation only for men. At least in part, a social information processing bias could contribute to hostile persons' perceptions of their social networks.

Sign in / Sign up

Export Citation Format

Share Document