Measuring and mitigating fake news on Facebook
Overview
This question evaluates statistical measurement and machine-learning model evaluation competencies, covering prevalence estimation, sampling design, bias correction, exposure-weighted metrics, and operational constraints for misinformation measurement on an online social platform.
Meta
Jul 12, 2025, 6:59 PM
Data Scientist
Onsite
Analytics & Experimentation
61
0
Measuring Fake-News Prevalence Under Reviewer Constraints
Context
Policy teams need an overnight view of fake-news prevalence on the platform, but only a small number of human reviewers are available. In parallel, leadership wants a long-term, statistically sound measurement program and a plan to improve detection models. Assume you can use an existing ML model to pre-score content as likely fake or not, and you can access impression counts to estimate user exposure.
Report and reason about both:
- Content prevalence: percentage of posts that are fake.
- Exposure prevalence: percentage of user impressions on fake content.
Questions
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Rapid overnight measurement: With limited reviewers, how would you measure fake-news impact within a single day?
- Hint: Use ML pre-labels plus targeted human sampling.
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Extrapolation from a sample: A random sample of 1,000 posts shows 10% fake. How would you extrapolate and report platform-level prevalence?
- Hint: Include confidence intervals and, if sampling is not simple random, a weighted projection.
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Robust long-term program: With ample resources, design a rigorous approach to quantify fake-news prevalence.
- Hint: Stratified sampling, user exposure weighting, and propagation/cascade analysis.
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Model iteration: Your detection model misses fake content. How would you iterate and improve it?
- Hint: Hard-negative mining, active learning, and ensemble models.
Solution
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