Identify Metrics to Detect Fake-Account Activity on Facebook

Quick Overview

This interview question evaluates statistical assumptions, formulas, estimation strategy, uncertainty, edge cases, and interpretation in a realistic interview setting. A strong answer for Identify Metrics to Detect Fake-Account Activity on Facebook states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

Identify Metrics to Detect Fake-Account Activity on Facebook

Company: Meta

Role: Data Scientist

Category: Statistics & Math

Difficulty: medium

Interview Round: Onsite

##### Scenario Facebook wants to understand and curb fake-account activity over the past month and at sign-up time. ##### Question List product or engagement metrics that could serve as proxies for month-over-month changes in fake-account activity. Which user-behavior signals would help distinguish fake from real accounts post-registration? Can we flag a fake account during the registration flow? Propose features and quick checks. Given a prior fake-account rate of 2% and a binomial observation of 18 flags in 600 new accounts, compute the posterior probability the next account is fake (clearly show Bayesian steps). To evaluate a fake-account classifier, would you optimize precision or recall? Discuss pros, cons, and the wider impact on Facebook. ##### Hints Frame the Bayesian update, assume Beta-Binomial conjugacy, and relate precision/recall trade-offs to user experience and integrity.

Overview: This interview question evaluates statistical assumptions, formulas, estimation strategy, uncertainty, edge cases, and interpretation in a realistic interview setting. A strong answer for Identify Metrics to Detect Fake-Account Activity on Facebook states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

|Home/Statistics & Math/Meta
Meta logo
Meta
Aug 4, 2025
mediumData ScientistOnsiteStatistics & Math
4
0

Identify Metrics to Detect Fake-Account Activity on Facebook

Detecting and Measuring Fake Accounts

Scenario

Facebook wants to understand and curb fake-account activity both over the past month and at sign-up time.

Tasks

  1. Month-over-month proxies: List product or engagement metrics that could indicate changes in fake-account activity.
  2. Post-registration signals: Identify behavioral signals that help distinguish fake from real accounts after signup.
  3. Registration-time detection: Can we flag a fake account during the registration flow? Propose features and quick checks.
  4. Bayesian update: Given a prior fake-account rate of 2% and observing 18 flags in a cohort of 600 new accounts, compute the posterior probability that the next account is fake. Use Beta–Binomial conjugacy and show steps.
  5. Classifier objective: For evaluating a fake-account classifier, would you optimize precision or recall? Discuss the trade-offs and wider impact on Facebook.

Clarifying Questions to Ask Guidance

  • Clarify the random variables, distributional assumptions, independence assumptions, and desired output.
  • Show enough derivation for the interviewer to follow the reasoning.
  • Explain how you would validate the result with simulation or sensitivity checks.

What a Strong Answer Covers Guidance

  • A correct setup with definitions, formulas, and boundary conditions.
  • A step-by-step derivation or estimation plan.
  • Interpretation of the result, including uncertainty and practical limitations.
  • Checks for assumptions, edge cases, and numerical stability.

Follow-up Questions Guidance

  • How would the result change if the assumptions were relaxed?
  • Can you verify the answer with a simulation?
  • What is the most likely source of estimation error?
Loading comments...