Due to the spread of SARS-CoV-2 virus infection and COVID-19 disease, there is an urgent need to analyze epidemic perception in Germany. This would enable authorities for preparation of specific actions minimizing public health and economic risks. The aim of this article is to singal possible research activities for future research.
The aim of this study is to quantitatively investigate perception of COVID-19 in German Media (Twitter, Google, Youtube and selection of news articles) in the Internet by infodemiological approach. We proposed quantitative Media analysis as Retrospective and for future Prospective observatory analysis of secondary data. We attempt to analyze main discourses via natural language processing tools (such as topic modelling and sentiment analysis), multilayer and temporal network analysis of accounts/words/topics and time series analysis.
There were just a few previous works quantitatively linking Internet activities and risk perception of infectious diseases in Germany. Traditional and social media do not only reflect reality, but also create it. German authorities, having a reliable analysis of the perception of the problem, could optimally prepare and manage the social dimension of the epidemic.
The analysis of electronic media makes it possible to analyze the problem perception in Germany and early detect possible behavioral changes (e.g. fear, anxiety) associated with the epidemic, which is crucial for a targeted response and tailored containment scenarios to minimize public health risks. Mistrust of governmental measures implementation has fulled Querdenken movement - an unlikely alliance of far-right and left-wing, as well as conspiracy theorists.
Being aware of many shortcomings of computational/digital epidemiology and its exploratory approach, it provides us with an opportunity to analyze a huge amount of digital footprint data at low cost and in a short time.
There is no confict of interest.