Risk Perception of COVID-19 in the German Internet Media and its epidemiological consequences during first wave of infections - exploration of possible research topic
Bibliographic record
Abstract
Abstract 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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".