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Meta·Software Engineer·Technical Phone Screen·Intermediate

Intermediate
Apr 2026

Summary

Interviewed for a data science role at Meta and got hit with a product analytics question about fake news that was deceptively open-ended. Not a lot of structure to the original prompt so I'm piecing this together from what I remember.

Questions Asked (1)

Q1

How would you measure and evaluate the impact of fake news on Facebook users?

Product Analytics & MetricsA/B Testing & ExperimentationProduct Sense & Ideation
Author's notes

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AI HintsAI Generated

Suggested Approach

Start by clarifying the goal: define what 'impact' means (e.g., belief in false information, engagement, trust) and the scope (e.g., specific user segments). Then propose a mix of quantitative metrics (e.g., prevalence, engagement, survey-based belief) and qualitative methods (e.g., user interviews), and suggest A/B testing to measure causal effects of interventions.

Pro tip: Acknowledge the tension between reducing fake news and not harming engagement or free expression, and propose measuring both intended and unintended consequences. This shows product maturity and aligns with Meta's values.

1. Define Objectives and Scope

Clarify what 'impact' means: exposure, belief, sharing, or trust. Specify the user segments and time frame to focus the analysis.

2. Select Metrics

Choose a combination of behavioral metrics (e.g., prevalence of fake news in feed, engagement rates) and perception metrics (e.g., survey-based belief accuracy, trust in Facebook).

3. Design Measurement Methods

Use A/B testing to measure causal effects of interventions (e.g., labeling, downranking). Complement with observational studies and user surveys for broader impact.

4. Analyze and Interpret

Compare metrics across control and treatment groups, segment by user demographics, and assess trade-offs (e.g., reduced engagement vs. reduced fake news belief).

5. Iterate and Communicate

Use findings to refine interventions, and communicate results with clear caveats about limitations and ethical considerations.

Key Points to Mention

  • Define fake news operationally (e.g., false or misleading content) and distinguish from other harmful content.
  • Use a combination of behavioral metrics (e.g., prevalence, engagement, sharing) and perception metrics (e.g., survey-based belief accuracy).
  • Leverage A/B testing to measure causal impact of interventions like labeling or downranking.
  • Consider unintended consequences: reduced engagement, censorship concerns, and impact on trust.
  • Segment analysis by user demographics, political leaning, and geography to understand differential impact.
  • Acknowledge limitations: self-report bias, difficulty in measuring belief change, and external validity.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.