Establishing Infodemic Management in Germany: A Framework for Social Listening and Integrated Analysis to Report Infodemic Insights at the National Public Health Institute
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it