Investigation of Public Acceptance of Misinformation Correctionin Social Media Based on Sentiment Attributions:InfodemiologyStudy Using Aspect-Based Sentiment Analysis

Background: The proliferation of misinformation on social media is a significant concern due to its frequent occurrence andsubsequent adverse social consequences. Effective interventions for and corrections of misinformation have become a focal pointof scholarly inquiry. However, exploration of the underlying causes that affect the public acceptance of misinformation correctionis still important and not yet sufficient.Objective: This study aims to identify the critical attributions that influence public acceptance of misinformation correction byusing attribution analysis of aspects of public sentiment, as well as investigate the differences and similarities in public sentimentattributions in different types of misinformation correction.Methods: A theoretical framework was developed for analysis based on attribution theory, and public sentiment attributionswere divided into 6 aspects and 11 dimensions. The correction posts for the 31 screened misinformation events comprised 33,422Weibo posts, and the corresponding Weibo comments amounted to 370,218. A pretraining model was used to assess publicacceptance of misinformation correction from these comments, and the aspect-based sentiment analysis method was used toidentify the attributions of public sentiment response. Ultimately, this study revealed the causality between public sentimentattributions and public acceptance of misinformation correction through logistic regression analysis.Results: The findings were as follows: First, public sentiments attributed to external attribution had a greater impact on publicacceptance than those attributed to internal attribution. The public associated different aspects with correction depending on thetype of misinformation. The accuracy of the correction and the entity responsible for carrying it out had a significant impact onpublic acceptance of misinformation correction. Second, negative sentiments toward the media significantly increased, and publictrust in the media significantly decreased. The collapse of media credibility had a detrimental effect on the actual effectivenessof misinformation correction. Third, there was a significant difference in public attitudes toward the official government andlocal governments. Public negative sentiments toward local governments were more pronounced.Conclusions: Our findings imply that public acceptance of misinformation correction requires flexible communication tailoredto public sentiment attribution. The media need to rebuild their image and regain public trust. Moreover, the government playsa central role in public acceptance of misinformation correction. Some local governments need to repair trust with the public.Overall, this study offered insights into practical experience and a theoretical foundation for controlling various types ofmisinformation based on attribution analysis of public sentiment