Design Sampling Strategy to Estimate Fake News Proportion
Overview
This question evaluates skills in survey sampling design, prevalence estimation with weighting and clustering, inferential statistics for low-prevalence events, and impact analysis using behavioral KPIs and comparative methods like regression and matching, and it falls under the Statistics & Math and Data Science domain.
Meta
Aug 4, 2025, 10:55 AM
Data Scientist
Onsite
Statistics & Math
21
0
Estimating Fake News Prevalence and Impact on Facebook
Context
Management is concerned about the volume and impact of fake news on the platform. You are asked to design a statistically sound approach to:
- Estimate the proportion of fake news with confidence intervals.
- Identify and analyze user-interaction metrics that capture the impact of fake news on behavior.
Assume you can sample posts and impressions over a defined time window and obtain ground-truth labels via human review.
Tasks
- Sampling Strategy and Estimation
- Define the target estimand(s) (e.g., proportion of fake news among posts vs among impressions).
- Propose a random sampling design, including any stratification you would use. Justify strata and allocation.
- Describe how you would compute prevalence estimates and confidence intervals, accounting for weights and clustering.
- Include a sample size calculation and how you would handle low-prevalence scenarios.
- Impact Assessment via User-Interaction Metrics
- List the key behavioral KPIs (e.g., clicks, shares, dwell time) you would track to assess impact.
- Outline the statistical approach to compare fake vs non-fake content (significance testing, regression/matching controls, multiple comparisons).
Solution
Login required
Show