Our analysis will use data on a time frame starting January 2020 and finishing December 2020 and will be based on data from four data sources:
1. Qualitative policy coding
Based on published news articles online we will collect mitigation measures in each federal state in Germany and those of the federal government as we move through the various phases of the COVID-19 crisis. The product will be a dataset that can be used as a stand-alone piece of information or in conjunction with other data sources in the project.
2. Survey Data
For the first phase of the crisis we will use existing survey data from the GESIS panel and other data collections, as we assume this phase will be over by the time this project will start. During the project we will conduct online surveys in three waves using a panel, in order to be able to follow the same individuals over the time of the project. The first survey will be conducted in August 2020 (month 2 of the project) and the rest with a six months gap between them (February 2021 and August 2021) having the final survey on month 13 of the project.
3. Discourse analysis of public speeches and statements of societal actors
Selected speeches and statements by societal actors like trade associations, unions, civil society associations, religious communities in the three phases are analyzed. The same coding book as for the Twitter analysis is used.
4. Discourse analysis of social media, namely Twitter
The continuous collection of Twitter data at GESIS has accumulated roughly 10 Bn Tweets since 2013 and also recorded about 10 M tweets per day during the current Corona crisis. This amounts to an accumulated dataset of about 1 billion tweets within the period 01- 03/2020 alone, where a majority reflect the public perception and discourse of various stakeholders on COVID-19, related measures and factors. Figure 1 shows the daily frequency of tweets explicitly referring to the SARS-CoV-2 virus in the period 01/10/2019 to 31/03/2020, with a total number of 3,072,177 tweets. The tweets identified in this way can be examined, for example, with regard to the temporal evolution of the sentiment, connotation, or relevant, corona-associated topics. Beyond this explicit mention of SARS-CoV-2, a significant part of the discourse on Twitter in the above-mentioned period addresses relevant topics, effects or measures such as #curfew, #LockDown, #HomeOffice, #WFH ("working from home"), face masks, panic buying. The qualitative and quantitative analysis of such discourse facilitates a dynamic understanding of solidarity and trust within society and the effects of media or political events on solidarity and discourse.
The figure shows the daily frequency of tweets that explicitly refer to the SARS-CoV-2 virus in the period from 1 October 2019 to 31 March 2020, with a total of 3,072,177 tweets.