Depicting the Emotion Flow: Super-Spreaders of Emotional Messages on Weibo During the COVID-19 Pandemic
Jingjing Yi, Jiayu Gina Qu, Wanjiang Jacob Zhang
Abstract
This study collected 2 million posts and reposts regarding the early stage of COVID-19 in China on Weibo from 26 December 2019 to 29 February 2020. Emotion analysis and social network analysis were used to examine the flow of emotional messages (emotion flow) by comparing them with the flow of general messages (information flow). Results indicated that both emotional messages and general messages present a multilayer diffusion pattern and follow network step flow models. In our dataset, emotion network has a higher transmission efficiency than information network; officially verified accounts were more likely to become super-spreaders of emotional messages; good emotions were predominant but isolated from other six emotions (joy, sadness, fear, disgust, surprise, anger) in online discussions; finally, government played a vital role in spreading good emotions.