JMIR Publications

ABSTRACT

Background:

The prevalence of health misinformation on social media is a complex and pressing issue. Addressing this issue is a commendable pursuit, but its effectiveness is often hindered by the diverse ways in which individuals interpret and understand information.

Objective:

This study identified the attributes of correction posts and categories of user engagement by investigating (1) the trend of user engagement with health misinformation correction during three years of the COVID-19 pandemic (2020-2022); (2) the relationship between post attributes and user engagement in sharing and reactions; and (3) the content generated by user comments serving as additional information attached to the post, affecting user engagement in sharing and reactions.

Methods:

Data were collected from the Facebook pages of a fact-checking organization and a health agency from January 2020 to December 2022. A total of 1,424 posts and 67,905 corresponding comments were analyzed. The posts were manually annotated by developing a research framework based on the fuzzy trace theory, categorizing information into "gist" and "verbatim" representations. Three types of gist representations were examined: "risk: risks associated with misinformation," "awareness: awareness of misinformation," and "value: value in health promotion." Further, three types of verbatim representations were identified: "numeric: numeric and statistical bases for correction"; "authority: authority from experts, scholars, or institutions"; and "facts: facts with varying levels of detail." The basic indicators of user engagement included shares, reactions, and comments as the primary dependent variables. Moreover, this study examined user comments and classified engagement as cognitive (knowledge-based, critical, and bias-based) or emotional (positive, negative, and neutral). Statistical analyses were performed to explore the impact of post attributes on user engagement.

Results:

Over time, the number of shares and reactions decreased, while comments reached their highest point in the second year before dropping to the lowest level in the third year. Bias-based engagement comprised a larger portion of the discussion during the second and third years. Regression models examining the relationship between the attributes of correction posts and user engagement categories revealed results aligning with the theory, indicating that all three types of gist representations significantly predicted shares. Awareness also significantly predicted reactions and comments, demonstrating that emphasizing the gist enhanced user engagement. When incorporating the types of comments into the model, bias-based engagement negatively impacted shares.

Conclusions:

This study presents a practical framework for understanding and examining how people react to misinformation corrections. The findings could guide efforts to design effective messages to address health misinformation and foster a more knowledgeable online environment. These practical implications make this study highly relevant and applicable in the field of health communication.