Evaluate Impact of Increasing Stranger Content in Feeds

Quick Overview

This interview question evaluates metric design, causal reasoning, experiment setup, diagnostics, SQL/statistical checks, and recommendations in a realistic interview setting. A strong answer for Evaluate Impact of Increasing Stranger Content in Feeds states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

Evaluate Impact of Increasing Stranger Content in Feeds

Company: Meta

Role: Data Scientist

Category: Analytics & Experimentation

Difficulty: medium

Interview Round: Technical Screen

##### Scenario Feed-ranking strategy: users interact more with friends’ posts; consider surfacing more stranger content. ##### Question Given evidence that users are more social when interacting with friends’ posts, would you increase the proportion of unconnected (stranger) posts in their feed? Outline how you would evaluate and decide. ##### Hints Discuss hypotheses, success metrics, experiment design, user segmentation, and potential trade-offs (engagement vs. discovery).

Quick Answer: This interview question evaluates metric design, causal reasoning, experiment setup, diagnostics, SQL/statistical checks, and recommendations in a realistic interview setting. A strong answer for Evaluate Impact of Increasing Stranger Content in Feeds states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

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Aug 4, 2025, 10:55 AM
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Evaluate Impact of Increasing Stranger Content in Feeds

Feed-Ranking Strategy: Friends vs. Stranger Content

Background

A personalized feed currently mixes posts from users' friends and posts from unconnected accounts ("strangers"). Observational data suggests users exhibit more social behavior (e.g., comments, shares, replies, messages) when viewing friends’ posts. The product team is considering surfacing a higher proportion of stranger content to increase discovery.

Task

Would you increase the proportion of stranger posts in the feed? Outline how you would evaluate and decide.

Requirements

  • State clear hypotheses and mechanisms.
  • Define success metrics, guardrails, and decision criteria.
  • Propose an experiment design (including dosage levels, duration, and power).
  • Specify key user and market segments to analyze.
  • Discuss trade-offs (e.g., engagement vs. discovery) and long-term effects.

Constraints & Assumptions

  • Preserve the scope, facts, inputs, and requested outputs from the prompt above.
  • If the prompt leaves a detail unspecified, state a reasonable assumption before relying on it.
  • Keep the answer interview-ready: concise enough to present, but concrete enough to implement or evaluate.

Clarifying Questions to Ask Guidance

  • Clarify the business objective, unit of analysis, time window, exposure definition, and primary metric.
  • State assumptions about instrumentation, randomization, sample size, and data quality.
  • Separate descriptive analysis from causal claims.

What a Strong Answer Covers Guidance

  • A metric framework with primary, guardrail, and diagnostic metrics.
  • A credible analysis or experiment design with clear assumptions and bias checks.
  • SQL/statistical logic for segmentation, variance, confidence, and data validation where relevant.
  • An actionable recommendation that explains trade-offs and next steps.

Follow-up Questions Guidance

  • What sanity checks would you run before trusting the result?
  • How would you handle novelty effects, seasonality, or selection bias?
  • What decision would you make if metrics disagree?
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