Accepted for/Published in: Journal of Medical Internet Research
Date Submitted: Jun 28, 2023
Date Accepted: Jun 19, 2024
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Why correction is ineffective? Investigation of public recognition for misinformation correction in social media using aspect-based sentiment analysis
ABSTRACT
Background:
The proliferation of misinformation in social media is a significant concern due to its frequent occurrence and subsequent adverse social consequences. The effective interventions and corrections of misinformation have become a focal point of scholarly inquiry.
Objective:
This study aimed to identify the critical attributions that influence public recognition for misinformation correction by utilizing attribution analysis of public aspect sentiment.
Methods:
A theoretical framework was developed for analysis based on cognitive appraisal theory and attribution theory. A pretraining model and aspect-based sentiment analysis were employed to reveal the causality between public sentiment attributions and public recognition for misinformation correction.
Results:
The findings were as follows: Firstly, public sentiments attributed to external attribution had a greater impact on public recognition than internal attribution. The public associated different aspects with correction depending on the type of misinformation. The accuracy of the correction and the entity responsible for carrying it out had a significant impact on public recognition for misinformation correction. Secondly, negative sentiments towards media had significantly increased and public trust in media had significantly decreased. The collapse of media credibility had a detrimental effect on the actual effectiveness of misinformation correction. Thirdly, there was a significant difference in public attitudes towards official government and local governments. Public negative sentiments towards local governments were more pronounced.
Conclusions:
Public recognition for misinformation correction require flexible communication tailored to public sentiment attribution. Media need to rebuilt their images and regain public trust. Moreover, the government plays a central role in public recognition for correction. Some local governments need to repair trust with the public. Overall, this study offered insight into practical experience and a theoretical foundation for governing various types of misinformation based on the analysis of public recognition.
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