Analyze User-Comment Distribution to Understand Engagement

Quick Overview

Analyze User-Comment Distribution to Understand Engagement evaluates metric design, causal reasoning, experiment setup, diagnostics, SQL/statistical checks, and recommendations in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

Analyze User-Comment Distribution to Understand Engagement

Company: Meta

Role: Data Scientist

Category: Analytics & Experimentation

Difficulty: medium

Interview Round: Onsite

##### Scenario Meta DSPA analytics exercise – evaluating engagement via comment activity. ##### Question You are given post, comment, and user tables. How would you analyze the user-comment distribution to understand engagement? Which core metrics would you define and what statistical or experimental steps would you take if a new comment feature is launched? ##### Hints Think about comments per DAU, long-tail distribution, Gini, percentiles; pre/post comparison or A/B test to isolate causal impact.

Quick Answer: Analyze User-Comment Distribution to Understand Engagement evaluates metric design, causal reasoning, experiment setup, diagnostics, SQL/statistical checks, and recommendations in a realistic interview setting. A strong answer 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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Analyze User-Comment Distribution to Understand Engagement

Meta DSPA Analytics Exercise: Comment Engagement Distribution

Context

You have three canonical tables for a social product:

  • users(user_id, join_date, country, device, …)
  • posts(post_id, author_user_id, created_at, …)
  • comments(comment_id, post_id, commenter_user_id, created_at, parent_comment_id [nullable], is_deleted, …)

Assume timestamps are available to compute daily/weekly activity and that a user is “active” on a day if they view or create content (define precisely in your analysis). You want to understand engagement through the lens of comments and evaluate a new comment feature.

Tasks

  1. Analyze the distribution of user commenting to understand engagement patterns.
  2. Define core metrics that summarize comment activity and inequality/long-tail effects.
  3. If a new comment feature is launched, outline the statistical/experimental steps to isolate its causal impact.

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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