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Role of Socio-Demographic on Social Media User Believability and Attitude towards Veracity of News about Covid-19 Pandemic
International Journal of Modern Agriculture ; 9(3):1185-1202, 2020.
Article in English | Web of Science | ID: covidwho-1094986
ABSTRACT

Purpose:

Covid-19 is a phenomenon of enormous magnitude and relevance. Since the beginning of this health crisis citizens have been spending more time on social media to get updates about the evolving situation and it is impossible to undermine the possibility of users exposing themselves to fake news. The aim of the paper is to determine to what extent the socio-demographic variables influences the user believability towards a news and tendency to respond in form of like, comment and share. Design/

Method:

A sample of 234 respondents participated in the online survey administered to test the research model. The questionnaire reliability was measured using Cronbach's alpha and path analysis and correlation matrix was used to examine the suggested research model.

Findings:

The findings suggest the role of age, gender, educational level, religion and employment status and the extent to which they influence user believability and tendency to respond. Age and religion were found to be most significant predictors of user acceptance of a social media news and attitude to respond. Also, user believability influences the attitude to respond in a positive direction. Practical Implications This study contributes to society by addressing how the socio-demographics can affect user believability towards a news during pandemic situations. Originality/Value By applying theories, traditionally researchers have studied the impact of demographic factors that influences user acceptance of news on social media. We attempt to induce a model of how the socio-demographics variables influences the user believability and attitude to respond during Covid-19 pandemic.
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Collection: Databases of international organizations Database: Web of Science Language: English Journal: International Journal of Modern Agriculture Year: 2020 Document Type: Article

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Collection: Databases of international organizations Database: Web of Science Language: English Journal: International Journal of Modern Agriculture Year: 2020 Document Type: Article