Define engagement metrics and analyze comment distribution

You are a Data Scientist for a **video platform**. A PM asks you to: 1) **Define metrics for “engagement”** (they want a clear metric framework they can use in experiments). 2) Analyze **user comment distribution** and propose how you would monitor it over time. ## Part A — Engagement metric framework Propose: - **Primary metric(s)** (what you would optimize) - **Diagnostic metrics** (to explain movement) - **Guardrail metrics** (to prevent harmful changes) Be explicit about: - Unit of analysis (user-day, session, video-view, etc.) - How you’d handle heavy users / skew (mean vs median, winsorization, log transforms) - How you’d prevent gaming (spammy/low-quality engagement) ## Part B — Comment distribution Comments are known to be **heavy-tailed** (most users comment rarely; a small minority comment a lot). Describe: - What distributions you would compute (by user, by video, by cohort) - What slices you would look at (new vs returning users, content categories, geos) - How you would detect regressions or anomalies (e.g., bots, spam, ranking changes) - What experiment you would run if the goal is to increase “healthy” commenting, including key confounders and how you’d interpret results