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.