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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.

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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.

Define and Measure Effective Read on Newsfeed | Meta Interview Question

Define and Measure Effective Read on Newsfeed

Last updated: Mar 29, 2026

Quick Overview

This question evaluates a data scientist's competency in designing and operationalizing an "effective read" metric for heterogeneous newsfeed content, encompassing signal selection, cross-format normalization, core reporting metrics, and experimental validation.

Designing an "Effective Read" Metric for a Newsfeed

Scenario

You are tasked with defining and measuring an "effective read" for posts in a social newsfeed that includes text, images, videos, and link posts. The metric will be used both to report user value and as a potential label/feature for ranking.

Question

How would you define and measure an "effective read" on a newsfeed? Specify:

The operational definition and component signals (e.g., time-on-screen, viewport coverage, interactions).
Concrete thresholds or normalization rules across content types.
The core metrics you would report (e.g., rates, distributions, guardrails).
Validation and experimentation you would run to ensure the definition reflects true user value and is robust to gaming.

Consider duration, screen coverage, engagement follow-ups, baseline rates, and A/B tests to prove business impact.

Solution

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