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How to Validate Friends' Content Engagement Hypothesis?

Last updated: Mar 29, 2026

Quick Overview

This question evaluates a data scientist's competency in experimental design, metric selection, and causal inference within the analytics and experimentation domain.

  • medium
  • Meta
  • Analytics & Experimentation
  • Data Scientist

How to Validate Friends' Content Engagement Hypothesis?

Company: Meta

Role: Data Scientist

Category: Analytics & Experimentation

Difficulty: medium

Interview Round: Technical Screen

##### Scenario Product team wants to understand whether friends' content drives more social engagement and how to evaluate the introduction of unconnected content in the Info Stream. ##### Question The hypothesis is that friends' content is more 'social' and drives stronger engagement than unconnected content. Describe how you would validate this hypothesis using the provided data. Unconnected content will be introduced to the Info Stream for the first time. Focusing on metrics and experimentation, explain how you would measure whether this launch is successful. ##### Hints Think of A/B tests, treatment vs control, primary engagement metrics (CTR, reactions), guardrails (retention, time spent), and ways to segment by relationship.

Quick Answer: This question evaluates a data scientist's competency in experimental design, metric selection, and causal inference within the analytics and experimentation domain.

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Meta
Jul 12, 2025, 6:59 PM
Data Scientist
Technical Screen
Analytics & Experimentation
98
0

Evaluating Friends' vs Unconnected Content in the Info Stream

Context

The Info Stream currently prioritizes content from a user's friends. The product team plans to introduce recommended ("unconnected") content from accounts the user does not follow for the first time. The goal is to understand whether friends' content is inherently more "social" (e.g., comments, replies, reshares) than unconnected content, and to measure whether launching unconnected content improves overall engagement without harming core health metrics.

Assumptions (for clarity):

  • Impressions, clicks, reactions, comments, shares, hides, and time-on-content are logged per post impression.
  • Each impression is labeled with relationship_type ∈ {friend, friend-of-friend, group, page, unconnected} and content_type (e.g., photo, video, link).
  • Ranking is stable aside from the unconnected insertion logic.

Question

  1. Hypothesis: Friends' content is more "social" and drives stronger engagement than unconnected content. Describe how you would validate this hypothesis using the available data.
  2. Unconnected content will be introduced to the Info Stream for the first time. Focusing on metrics and experimentation, explain how you would measure whether this launch is successful.

Hints: Consider A/B tests (treatment vs. control), primary engagement metrics (CTR, reactions), guardrails (retention, time spent), and segmentation by relationship.

Solution

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