Dataset Information

The COVID-19-Social Identity-Digital Media Nexus in India: Polarization and Blame.

ABSTRACT: Drawing on social identity theory and research on digital media and polarization, this study uses a quasi-experimental design with a random sample (n = 3304) to provide causal evidence on perceptions of who is to blame for the initial spread of COVID-19 in India. According blame to three different social and political entities-Tablighi Jamaat (a Muslim group), the Modi government, and migrant workers (a heterogeneous group)-are the dependent variables in three OLS regression models testing the effect of the no-blame treatment, controlling for Facebook use, social identity (religion), vote in the 2019 national election, and other demographics. Results show respondents in the treatment group were more likely to allay blame, affective polarization (dislike for outgroup members) was social identity based, not partisan based, and Facebook/Instagram use was not significant. Congress and United Progressive Alliance voters in 2019 were less likely to blame the Modi government for the initial spread. Unlike extant research in western contexts, affective and political polarization appear to be distinct concepts in India where social identity complexity is important. This study of the first wave informs perceptions of blame in future waves, which are discussed in conclusion along with questions for future research.

SUBMITTER: Arabaghatta Basavaraj K

PROVIDER: S-EPMC8447430 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

The COVID-19-Social Identity-Digital Media Nexus in India: Polarization and Blame.

Arabaghatta Basavaraj Kiran K Saikia Pahi P Varughese Anil A Semetko Holli A HA Kumar Anup A

Political psychology 20210718 5

Drawing on social identity theory and research on digital media and polarization, this study uses a quasi-experimental design with a random sample (<i>n</i> = 3304) to provide causal evidence on perceptions of who is to blame for the initial spread of COVID-19 in India. According blame to three different social and political entities-Tablighi Jamaat (a Muslim group), the Modi government, and migrant workers (a heterogeneous group)-are the dependent variables in three OLS regression models testin ...[more]

PMID: 34548718

Similar Datasets

Project description:BackgroundSocial media and digital technologies have played essential roles in disseminating information and promoting vaccination during the COVID-19 pandemic. There is a need to summarize the applications and analytical techniques of social media and digital technologies in monitoring vaccine attitudes and administering COVID-19 vaccines.ObjectiveWe aimed to synthesize the global evidence on the applications of social media and digital technologies in COVID-19 vaccination and to explore their avenues to promote COVID-19 vaccination.MethodsWe searched 6 databases (PubMed, Scopus, Web of Science, Embase, EBSCO, and IEEE Xplore) for English-language articles from December 2019 to August 2022. The search terms covered keywords relating to social media, digital technology, and COVID-19 vaccines. Articles were included if they provided original descriptions of applications of social media or digital health technologies/solutions in COVID-19 vaccination. Conference abstracts, editorials, letters, commentaries, correspondence articles, study protocols, and reviews were excluded. A modified version of the Appraisal Tool for Cross-Sectional Studies (AXIS tool) was used to evaluate the quality of social media-related studies. The review was undertaken with the guidance of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews.ResultsA total of 178 articles were included in our review, including 114 social media articles and 64 digital technology articles. Social media has been applied for sentiment/emotion analysis, topic analysis, behavioral analysis, dissemination and engagement analysis, and information quality analysis around COVID-19 vaccination. Of these, sentiment analysis and topic analysis were the most common, with social media data being primarily analyzed by lexicon-based and machine learning techniques. The accuracy and reliability of information on social media can seriously affect public attitudes toward COVID-19 vaccines, and misinformation often leads to vaccine hesitancy. Digital technologies have been applied to determine the COVID-19 vaccination strategy, predict the vaccination process, optimize vaccine distribution and delivery, provide safe and transparent vaccination certificates, and perform postvaccination surveillance. The applied digital technologies included algorithms, blockchain, mobile health, the Internet of Things, and other technologies, although with some barriers to their popularization.ConclusionsThe applications of social media and digital technologies in addressing COVID-19 vaccination-related issues represent an irreversible trend. Attention should be paid to the ethical issues and health inequities arising from the digital divide while applying and promoting these technologies.

Project description:BackgroundIncreasing vaccination coverage was key to curbing the COVID-19 pandemic globally. However, lack of trust in the vaccine and fear of side effects in regions like the Caribbean resulted in a low uptake despite enough vaccine supply.MethodsWe conducted two correlational analyses and one experiment between five sequential behaviorally informed Facebook campaigns, social media performance outcomes, and district-level vaccination data. First, we ran multivariate linear regression models to estimate the mean differences between the campaigns in (i) social media performance ("Clicks" and "Engagement") and (ii) COVID-19 vaccination uptake at the district level. "Clicks" were measured by the number of people who clicked on the respective Facebook advert and visited the official vaccination site. "Engagements" were the number of people interacting with the advert through likes and emojis. Second, we took advantage of the experimental design during one of the campaigns to analyze the differential effect of messages conveying information about the number of people reporting vaccination side effects using words ("Few"/ "Majority) and numbers ("3 out of 100 ") on social media performance.ResultsThe correlational analysis showed that the number of "Clicks" and "Engagement\" was similar among campaigns, except for the campaign focusing on vaccines' effectiveness, which had 14.65 less clicks and 19.52 less engagements per advert (including controls and district-fixed effects) compared to the base "It's safe" campaign. Vaccination rates were highest at times coinciding with campaigns focusing on vaccination safety and effectiveness. Our experimental results showed that informational messages related to side effects that were framed using words ("Majority did not report discomfort"/ "Few persons reported discomfort") were better at generating "Clicks\" compared to those using numbers ("3 out of 100 reported discomforts").ConclusionsFacebook adverts highlighting vaccine safety had a similar level of social media performance as other campaigns, except for adverts focusing on vaccine efficacy, which performed worse. Communicating side-effect information with words instead of numbers can expand social media interest in low-uptake regions like the Caribbean. Our results serve as preliminary evidence for public health officials to encourage vaccine uptake in high-hesitancy contexts.

Dataset Information

The COVID-19-Social Identity-Digital Media Nexus in India: Polarization and Blame.

Publications

The COVID-19-Social Identity-Digital Media Nexus in India: Polarization and Blame.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets