Establishing Infodemic Management in Germany: A Framework for Social Listening and Integrated Analysis to Report Infodemic Insights at the National Public Health Institute
Bibliographic record
Abstract
BACKGROUND: To respond to the need to establish infodemic management functions at the national public health institute in Germany (Robert Koch Institute, RKI), we explored and assessed available data sources, developed a social listening and integrated analysis framework, and defined when infodemic management functions should be activated during emergencies. OBJECTIVE: We aimed to establish a framework for social listening and integrated analysis for public health in the German context using international examples and technical guidance documents for infodemic management. METHODS: This study completed the following objectives: identified (potentially) available data sources for social listening and integrated analysis; assessed these data sources for their suitability and usefulness for integrated analysis in addition to an assessment of their risk using the RKI's standardized data protection requirements; developed a framework and workflow to combine social listening and integrated analysis to report back actionable infodemic insights for public health communications by the RKI and stakeholders; and defined criteria for activating integrated analysis structures in the context of a specific health event or health emergency. RESULTS: We included and classified 38% (16/42) of the identified and assessed data sources for social listening and integrated analysis at the RKI into 3 categories: social media and web-based listening data, RKI-specific data, and infodemic insights. Most data sources can be analyzed weekly to detect current trends and narratives and to inform a timely response by reporting insights that include a risk assessment and scalar judgments of different narratives and themes. CONCLUSIONS: This study identified, assessed, and prioritized a wide range of data sources for social listening and integrated analysis to report actionable infodemic insights, ensuring a valuable first step in establishing and operationalizing infodemic management at the RKI. This case study also serves as a roadmap for others. Ultimately, once operational, these activities will inform better and targeted public health communication at the RKI and beyond.
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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 | 0.132 | 0.072 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.022 | 0.013 |
| Science and technology studies | 0.006 | 0.020 |
| Scholarly communication | 0.026 | 0.023 |
| Open science | 0.005 | 0.016 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".