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Analytics & Experimentation questions require understanding of core concepts and practice. PracHub provides solutions with explanations to help you master analytics & experimentation interviews.

What difficulty level is this interview question?

This is a medium difficulty Analytics & Experimentation question, commonly asked during Onsite rounds at Meta.

What role is this question designed for?

This question is commonly asked for Data Scientist candidates at Meta during technical interviews.

Analyze User-Comment Distribution to Understand Engagement | Meta Interview Question

Analyze User-Comment Distribution to Understand Engagement

Last updated: Mar 29, 2026

Quick Overview

This question evaluates a Data Scientist's competency in analytics and experimentation, covering distributional analysis of user commenting, definition of engagement and inequality metrics, and causal inference for feature impact within the Analytics & Experimentation domain.

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

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

Solution

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