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Identify Metrics to Detect Fake-Account Activity on Facebook

Last updated: Mar 29, 2026

Quick Overview

This question evaluates a Data Scientist's competency in statistical inference, anomaly detection metrics, behavioral feature engineering, and classifier trade-off analysis.

  • medium
  • Meta
  • Statistics & Math
  • Data Scientist

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.

Quick Answer: This question evaluates a Data Scientist's competency in statistical inference, anomaly detection metrics, behavioral feature engineering, and classifier trade-off analysis.

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Meta
Aug 4, 2025, 10:55 AM
Data Scientist
Onsite
Statistics & Math
2
0

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.

Solution

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