Third-Person Perception and Corrective Behaviors Toward Health Misinformation in Algorithm-Recommended Environments

Vollständiger Abstract

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During the outbreak of the novel coronavirus, the information environment has undergone tremendous changes, and the research on the spread of health misinformation has become very urgent. This study uses the third-person effect (TPE) theory and algorithmic literacy (AL) as an analytical framework to explore how users perceive the impact of health error information in the context of algorithmic recommendation, and whether individuals are willing to actively correct behavioral intentions. Therefore, this study combines third-party perception theory and algorithm literacy to explore the formation mechanism of users' corrective behavior intention. In this study, a national online questionnaire survey was conducted through the professional research institution Questionnaire Star, and a total of 208 respondents were collected. SPSS is used for statistical analysis of data and empirical testing of research hypotheses. The study found that respondents were more likely to think they were more susceptible to health misinformation than others. The traditional third-person perception theory does not show strong explanatory power in the health error information propagation scenario in the algorithm recommendation environment. The user's intention of corrective behavior may be mainly derived from the judgment of the degree of influence on others, and more related to the individual's understanding of the algorithm, the evaluation of corrective behavior, the influence of social norms and other factors. This study provides a new perspective for understanding the behavior mechanism and user cognition in the dissemination of health error information in the algorithmic environment.