3.2. Sampling and Data Collection
Data in this study were collected from the ‘Wuhan Release’ Sina Weibo account of the Wuhan government. It has firstly reported viral pneumonia of unknown causes on 31 December 2019. At this time, the online reaction of the public may be greatly intensified as they were faced with this severe condition suddenly. After referencing to other studies [27,93], with the help of python toolkit, we crawled all related posts during the period of 31 December to 19 April 2020, from the day that the earliest cases in Wuhan were reported, to the day that the number of confirmed, suspected cases were cleared. The data includes the publisher, title, text length, information source, number of reposts, comments, likes, and whether @somebody/#hashtag# is contained, which was acquired and stored according to the time. At the same time, the URLs of the pictures or videos uploaded were also captured, and we also determined the text type (i.e., whether or not the account was actively using pictures or videos). A total of 3596 posts were pertinent to COVID-19 after a manual check.
Most studies have evaluated engagement using quantitative indicators of social media platforms, like the number of repost, comments, and likes [94,95,96] (Bonson and Ratkai, 2013; Agostino and Arnaboldi, 2016; del Mar Galvez Rodriguez et al., 2019). These three indicators respectively reflect the engagement behavior of the users. For instance, the share (repost) activity in crisis could be seen as an informal recommendation system for the content [97,98]. Then we calculated the number of comments below each post, the public who reply want to express their concerns, respond to the authorities, and reveal subjective feelings [99]. Finally, we are concerned about the thump up behavior (likes), which could be regarded as a supportive behavior, as one of the public engagement factors. These data were the factors related to the citizen engagement (repost, comment, likes) that were studied through a statistical analysis with these factors related to the content type, dialogic loop, media richness, and text length et al., to understand the influence between citizen engagement and the government information release.