Political ideology and differences in seeking COVID-19 information on the internet: examining the comprehensive model of information seeking

Author:

Jin XianlinORCID

Abstract

PurposeGuided by the Comprehensive Model of Information Seeking (CMIS), this article identifies significant predictors that impact individuals seeking COVID-19 information. People with different political ideologies read contradictory information about the COVID-19 pandemic. However, how political ideology may affect COVID-19 information seeking remains unclear. This study explores the major information channels for individuals with different political ideologies to seek COVID-19 information. It further examines how political ideologies influence CMIS's effectiveness in predicting online health information-seeking.Design/methodology/approachThis study collected 394 completed survey responses from adults living in the United States after the 2020 lockdown. ANOVA analyses revealed the differences in salience, beliefs, information carrier characteristics, utilities and information-seeking actions between Liberals and Conservatives. Regression analyses discovered variables that predict Liberals' and Conservatives' online health information seeking.FindingsResults suggest that the internet is the top channel for COVID-19 information seeking. Compared to Conservatives, Liberals report more COVID-19 information-seeking actions. Liberals also express stronger salience, perceive higher trustworthiness of online COVID-19 information, are more likely to think of seeking online COVID-19 information as useful and helpful and report more substantial efficacy to mitigate the risk. Most CMIS variables predict Liberals' information seeking; however, only salience significantly predicts Conservatives' information seeking.Originality/valueThis article indicates that CMIS should include political ideology to refine its prediction of information seeking. These findings offer practical implications for designing health messages, enhancing information distribution and reducing the public's uncertainty.Peer reviewThe peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-08-2022-0436.

Publisher

Emerald

Subject

Library and Information Sciences,Computer Science Applications,Information Systems

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