3. Method

3.1. Research Case: COVID-19 Crisis in Wuhan
In December 2019, hospitals in Wuhan, Hubei province, China, identified several unknown pneumonia cases, which were later confirmed to be caused by a new type of coronavirus. Then the World Health Organization (WHO) renamed this disease, COVID-19 on 11 February 2020. This study took this public health crisis as a case due to its global pandemic nature and grave threat to human life and health. Wuhan, as the early city to be affected, has gradually recovered from the crisis. The experience from Wuhan would be helpful and enlightening for the other regions which still suffer the COVID-19 heavily. After the outbreak of COVID-19, official Weibo accounts were one of the most important sources of crisis-related information [89]. There were few studies focused on the information release and crisis response during the COVID-19 [27,90], but there is a lack the research about the communication behavior of the local government and the citizens’ reaction during the COVID-19. This study focuses on the official Weibo account Wuhan Release of Wuhan’s local government. There are three reasons for this: first, Weibo is one of the most popular SM platforms in China and has the first series of government accounts with a large number of followers (more than 3.78 million). Second, people prefer official news sources with strong authority and credibility during a crisis, and the Wuhan citizens were more likely to choose local media as their main source [33], thus Wuhan Release played an irreplaceable role in crisis communication to Wuhan citizens. Third, there exists the possibility that the government may regulate the content on social media by deleting or screening negative, sensitive, and extreme comments [91], which could influence the validity and reliability of the data from social media. We choose an official outlet is that it could relatively relieve the influence from the Internet censorship, that’s because the official media sifted through the information at the time of its release, and the research data would not be heavily influenced by the sensitivity of the topics discussed and the division of opinion [92].

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.

3.3. Content Analysis
Content analysis is defined as a multi-purpose research method to study a wide range of issues by systematically, complex, and objectively identifying characteristics of large sample data [100]. Followed the grounded theory approach, in spite of collecting data, we also need to generate concepts and topics from the ground up [29,101]. During this analysis, two trained coders conducted the coding work. The first step was defining the coding norms, and constructing categories as content type. After examining inter-coder reliability, the two coders started to analyzing data. The reporting results are shown in Table 1.