Exploring the contagion routes of digital emotion on health topics: A multidimensional configuration analysis pre-post rumor refutation

Health topics are consistently prevalent on social media and are highly susceptible to rumor. The effectiveness of rumor refutation in fostering positive shifts in public opinion remains to be empirically validated. On social media platforms, digital emotion contagion significantly influences netizens’ attitudes toward health topics and the reception of information aimed at rumor refutation. The Weibo platform is employed to gather extensive user interaction data, and authoritative rumor refutation information is selected as key nodes. Based on digital emotion contagion theory and tipping point theory, this study examines the multidimensional factors influencing digital emotion contagion in the dissemination of health topics. Sentiment analysis (SA) and social network analysis (SNA) are employed to assign values to variables. This study performs a multidimensional configuration analysis of the digital emotional contagion process pre- and post-refutation. Prior to refutation, topic type triggers positive digital emotions among rational netizens, whereas opinion leaders are more likely to elicit such emotions among conformist netizens. When opinion leaders are present and network density is high, negative digital emotions are more likely to propagate. The effectiveness of official institutions in rumor refutation is determined by various factors, including topic type, the role of opinion leaders, the emotional tone of the content, and network density. These factors collectively help suppress the dissemination of negative digital emotions. Research suggests that official refutations of rumors do not always lead to an improvement in digital emotional states; rather, the more prevalent a health topic becomes, the more pronounced the effect of refutation. When the topic is widely disseminated and the audience’s values are aligned, refutation can positively influence digital emotions. An expandable analytical framework is offered, establishing a new foundation for investigating how to promptly correct rumors and effectively guide digital emotions during the dissemination of health topics.