Background Various online rumors have led to inappropriate behaviors among the public in response to the COVID-19 epidemic in China. These rumors adversely affect people’s physical and mental health. Therefore, a better understanding of the relationship between public emotions and rumors during the epidemic may help generate useful strategies for guiding public emotions and dispelling rumors. Objective This study aimed to explore whether public emotions are related to the dissemination of online rumors in the context of COVID-19. Methods We used the web-crawling tool Scrapy to gather data published by People’s Daily on Sina Weibo, a popular social media platform in China, after January 8, 2020. Netizens’ comments under each Weibo post were collected. Nearly 1 million comments thus collected were divided into 5 categories: happiness, sadness, anger, fear, and neutral, based on the underlying emotional information identified and extracted from the comments by using a manual identification process. Data on rumors spread online were collected through Tencent’s Jiaozhen platform. Time-lagged cross-correlation analyses were performed to examine the relationship between public emotions and rumors. Results Our results indicated that the angrier the public felt, the more rumors there would likely be (r=0.48, P<.001). Similar results were observed for the relationship between fear and rumors (r=0.51, P<.001) and between sadness and rumors (r=0.47, P<.001). Furthermore, we found a positive correlation between happiness and rumors, with happiness lagging the emergence of rumors by 1 day (r=0.56, P<.001). In addition, our data showed a significant positive correlation between fear and fearful rumors (r=0.34, P=.02). Conclusions Our findings confirm that public emotions are related to the rumors spread online in the context of COVID-19 in China. Moreover, these findings provide several suggestions, such as the use of web-based monitoring methods, for relevant authorities and policy makers to guide public emotions and behavior during this public health emergency.
No abstract
BACKGROUND In the present study, we aim to explore whether public emotions are related to the dissemination of emotional online comments and at times online rumors in the context of COVID-19. OBJECTIVE To explore whether public emotions are related to the dissemination of emotional online comments and at times online rumors in the context of COVID-19. METHODS We used Scrapy to gather the data from Weibo published by People's Daily after January 8, 2020, and netizens’ comments under each Weibo post. Nearly one million comments were divided into five categories (anger, fear, happiness, sadness, and neutral) according to the emotional information in these contents by manual identification. Rumors data was collected through a platform “Tencent myth busters”. Cross-correlations analysis was used to examine the relationship between public emotions and rumors. RESULTS The results indicated that the angrier the public got, the more rumors there would be. Similar findings were found in the relationship between fear and rumors and the relationship between sadness and rumors. Furthermore, we found that happiness would lag behind by one day with the increase of rumors. In addition, our data showed that there was a significant positive correlation between fear and fearful rumors. CONCLUSIONS Our findings provide supportive evidence to the relationship between Public Emotions and Rumors in the Context of COVID-19.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.