Ergoic framing in New Right online groups

Author(s):  
Ondřej Procházka ◽  
Jan Blommaert

Abstract Conspiracy theories are often disqualified as inadequate and deliberate forms of misinformation. In this analysis, we engage with a specific case, the conspiracy theory developed on an online New Right forum called Q about the so-called “MAGA Kid incident” with focus on its circulation and uptake on Facebook. Drawing on ethnomethodological principles, the analysis shows how ergoic argumentation is systematically being deployed as a means of debunking rational-factual discourses about such incidents. While rationality itself is being rejected, conspiracy theorists deploy “reasonable” knowledge tactics. The paper shows how conspiracy theorists skillfully mobilize social media affordances, particularly Internet memes, to promote conspiracism as a form of inclusive political activism as well as a legitimate and “critical” mode of reasoning.

10.2196/26527 ◽  
2021 ◽  
Vol 7 (4) ◽  
pp. e26527
Author(s):  
Dax Gerts ◽  
Courtney D Shelley ◽  
Nidhi Parikh ◽  
Travis Pitts ◽  
Chrysm Watson Ross ◽  
...  

Background The COVID-19 outbreak has left many people isolated within their homes; these people are turning to social media for news and social connection, which leaves them vulnerable to believing and sharing misinformation. Health-related misinformation threatens adherence to public health messaging, and monitoring its spread on social media is critical to understanding the evolution of ideas that have potentially negative public health impacts. Objective The aim of this study is to use Twitter data to explore methods to characterize and classify four COVID-19 conspiracy theories and to provide context for each of these conspiracy theories through the first 5 months of the pandemic. Methods We began with a corpus of COVID-19 tweets (approximately 120 million) spanning late January to early May 2020. We first filtered tweets using regular expressions (n=1.8 million) and used random forest classification models to identify tweets related to four conspiracy theories. Our classified data sets were then used in downstream sentiment analysis and dynamic topic modeling to characterize the linguistic features of COVID-19 conspiracy theories as they evolve over time. Results Analysis using model-labeled data was beneficial for increasing the proportion of data matching misinformation indicators. Random forest classifier metrics varied across the four conspiracy theories considered (F1 scores between 0.347 and 0.857); this performance increased as the given conspiracy theory was more narrowly defined. We showed that misinformation tweets demonstrate more negative sentiment when compared to nonmisinformation tweets and that theories evolve over time, incorporating details from unrelated conspiracy theories as well as real-world events. Conclusions Although we focus here on health-related misinformation, this combination of approaches is not specific to public health and is valuable for characterizing misinformation in general, which is an important first step in creating targeted messaging to counteract its spread. Initial messaging should aim to preempt generalized misinformation before it becomes widespread, while later messaging will need to target evolving conspiracy theories and the new facets of each as they become incorporated.


2021 ◽  
Author(s):  
Ikpe Justice Akpan ◽  
Obianuju Genevieve Aguolu ◽  
Yawo Mamoua Kobara ◽  
Rouzbeh Razavi ◽  
Asuama A Akpan ◽  
...  

BACKGROUND The use of the internet and web-based platforms to obtain public health information and manage health-related issues has become widespread in this digital age. The practice is so pervasive that the first reaction to obtaining health information is to “Google it.” As SARS-CoV-2 broke out in Wuhan, China, in December 2019 and quickly spread worldwide, people flocked to the internet to learn about the novel coronavirus and the disease, COVID-19. Lagging responses by governments and public health agencies to prioritize the dissemination of information about the coronavirus outbreak through the internet and the World Wide Web and to build trust gave room for others to quickly populate social media, online blogs, news outlets, and websites with misinformation and conspiracy theories about the COVID-19 pandemic, resulting in people’s deviant behaviors toward public health safety measures. OBJECTIVE The goals of this study were to determine what people learned about the COVID-19 pandemic through web searches, examine any association between what people learned about COVID-19 and behavior toward public health guidelines, and analyze the impact of misinformation and conspiracy theories about the COVID-19 pandemic on people’s behavior toward public health measures. METHODS This infodemiology study used Google Trends’ worldwide search index, covering the first 6 months after the SARS-CoV-2 outbreak (January 1 to June 30, 2020) when the public scrambled for information about the pandemic. Data analysis employed statistical trends, correlation and regression, principal component analysis (PCA), and predictive models. RESULTS The PCA identified two latent variables comprising past coronavirus epidemics (pastCoVepidemics: keywords that address previous epidemics) and the ongoing COVID-19 pandemic (presCoVpandemic: keywords that explain the ongoing pandemic). Both principal components were used significantly to learn about SARS-CoV-2 and COVID-19 and explained 88.78% of the variability. Three principal components fuelled misinformation about COVID-19: misinformation (keywords “biological weapon,” “virus hoax,” “common cold,” “COVID-19 hoax,” and “China virus”), conspiracy theory 1 (ConspTheory1; keyword “5G” or “@5G”), and conspiracy theory 2 (ConspTheory2; keyword “ingest bleach”). These principal components explained 84.85% of the variability. The principal components represent two measurements of public health safety guidelines—public health measures 1 (PubHealthMes1; keywords “social distancing,” “wash hands,” “isolation,” and “quarantine”) and public health measures 2 (PubHealthMes2; keyword “wear mask”)—which explained 84.7% of the variability. Based on the PCA results and the log-linear and predictive models, ConspTheory1 (keyword “@5G”) was identified as a predictor of people’s behavior toward public health measures (PubHealthMes2). Although correlations of misinformation (keywords “COVID-19,” “hoax,” “virus hoax,” “common cold,” and more) and ConspTheory2 (keyword “ingest bleach”) with PubHealthMes1 (keywords “social distancing,” “hand wash,” “isolation,” and more) were <i>r</i>=0.83 and <i>r</i>=–0.11, respectively, neither was statistically significant (<i>P</i>=.27 and <i>P</i>=.13, respectively). CONCLUSIONS Several studies focused on the impacts of social media and related platforms on the spreading of misinformation and conspiracy theories. This study provides the first empirical evidence to the mainly anecdotal discourse on the use of web searches to learn about SARS-CoV-2 and COVID-19.


2021 ◽  
pp. 1532673X2110135
Author(s):  
Seong Jae Min

A survey of 3,441 U.S. social media users showed that a high portion believes in conspiracy theories, and their beliefs vary widely along the party lines and socio-demographic factors. In particular, conservative conspiracy theories were more pronounced than liberal ones, and older White males with high conservatism and Protestantism showed higher endorsement of conservative conspiracy theories. Furthermore, ideological conservatives who frequently discuss politics showed higher association with a conservative conspiracy theory than conservatives who discuss politics less frequently. However, network diversity moderated the interaction of conservative ideology and political discussion such that conservatives who discuss politics frequently in a relatively heterogeneous social media network setting had lower beliefs in a conspiracy theory than conservatives who do so in a more homogeneous network.


2021 ◽  
Author(s):  
Gabriel Doyle

In our present era of fractured politics, social media, and fake news, conspiracy theories are as prominent as ever. While conspiracy theories are often dismissed as pathological or irrational reasoning, belief in at least some conspiracy theories could arise from a Bayesian rational system that is merely wrong, rather than truly irrational. This paper lays out a framework for understanding how conspiracy theories could arise from rational Bayesian cognition, identifying four potential sources for conspiracy theory belief in a primarily rational framework: elevated prior belief in CTs, different likelihoods, missing non-conspiratorial explanations, and non-epistemic utilities.


2021 ◽  
Vol 11 (2) ◽  
pp. 43-55
Author(s):  
Teija Sederholm ◽  
Petri Jääskeläinen ◽  
Aki-Mauri Huhtinen

Disinformation and misinformation about COVID-19 have proliferated, particularly on social media. The purpose of this paper is to show the rhizomic nature of COVID-19-related dis- and misinformation having aspect of conspiracy theories, which are used on social media platforms to counter the official narratives about the origins of the virus. Consisting of 40 news-style articles, the data was used to find out how a conspiracy theory about the virus being a possible man-made bioweapon was presented to the audience. The results indicate that the rhizomatic structure of COVID-19 conspiracy theories makes it possible to vary the narratives based on the platform where it is published and the target audience. Information spreads in unexpected ways, and it is difficult to control or predict the spread of extremist content. This makes it possible for different actors, governments, and organizations to use information for their own purposes as a weapon of information warfare.


2020 ◽  
Author(s):  
Marios Constantinou ◽  
Anthony Kagialis ◽  
Maria Karekla

Abstract Science may be failing to convince a significant number of people about COVID-19 scientific facts and needed public health measures. Individual and social factors are behind believing conspiracy theories. Adults (N = 1001) were asked to rate their beliefs in various conspiracy theories circulating in social media, rate their psychological distress relating to COVID-19, rate their trust in science to solve COVID-19 problems, and rate their willingness to adhere to measures regarding social distancing and quarantine. The findings showed conspiracy theories are widely believed even among highly educated individuals. Stronger conspiracy theory beliefs predicted science mistrust and unwillingness to adhere to public health measures. Psychological distress increased conspiracy beliefs. Recommendations, stemming from the findings, for reducing such beliefs and better serve public health are discussed.


Author(s):  
Benjamin J. Dow ◽  
Amber L. Johnson ◽  
Cynthia S. Wang ◽  
Jennifer Whitson ◽  
Tanya Menon

2021 ◽  
Vol 24 (2) ◽  
pp. 270-275 ◽  
Author(s):  
Karen M. Douglas

Conspiracy theories started to appear on social media immediately after the first news about COVID-19. Is the virus a hoax? Is it a bioweapon designed in a Chinese laboratory? These conspiracy theories typically have an intergroup flavour, blaming one group for having some involvement in either manufacturing the virus or controlling public opinion about it. In this article, I will discuss why people are attracted to conspiracy theories in general, and why conspiracy theories seem to have flourished during the pandemic. I will discuss what the consequences of these conspiracy theories are for individuals, groups, and societies. I will then discuss some potential strategies for addressing the negative consequences of conspiracy theories. Finally, I will consider some open questions for research regarding COVID-19 conspiracy theories, in particular focusing on the potential impact of these conspiracy theories for group processes and intergroup relations.


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