[Analytics Reasoning] Impact of Malicious Accounts on Meta
Overview
This question evaluates a data scientist's competency in probabilistic reasoning, classifier performance interpretation (sensitivity and specificity), sampling and experiment design, feature engineering for fraud detection, and assessment of user-impact from malicious accounts on a social network.
Meta
Apr 7, 2025, 3:24 AM
Data Scientist
Onsite
Analytics & Experimentation
7
0
Social Network: Friend-Request Risk and Bad-Account Detection
Context and Assumptions
- On a large social network (similar to Facebook), 1% of accounts are malicious ("bad").
- Bad accounts send friend requests at a rate 10× higher than good accounts.
- A binary classifier achieves 95% true positive rate (TPR) and 95% true negative rate (TNR).
- Unless otherwise noted, treat incoming friend requests as independent draws from the population of senders over a short time window (stationary behavior).
Questions
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Single Friend Request Probability
- What is the probability that a received friend request comes from a bad account?
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Multiple Friend Requests
- What is the probability that, out of five friend requests, at least one originates from a bad account?
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Model Reliability
- If the model flags an account as bad, how likely is it truly malicious (given TPR = 95% and TNR = 95%)? State assumptions about which population you’re flagging (all accounts vs. request senders).
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Data and Features
- Which data types (behavior logs, friend-request patterns, reported incidents, etc.) are most relevant to classify accounts accurately?
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Assessing Bad-Account Prevalence
- Propose methods (e.g., stratified or random sampling) to determine whether the bad-account issue is substantial enough to warrant intervention.
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Defining a "Bad User"
- What characteristics or behaviors would qualify an account as "bad" (e.g., spammer, scammer, bot)?
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Platform Impact
- How could malicious users affect platform trust, user experience, and reputation?
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Friend Request Implications
- How might frequent friend requests from bad accounts impact legitimate users and community health?
Follow-Up Considerations
- Threshold Tuning: How does adjusting the decision threshold change false positives and false negatives?
- Cost–Benefit Analysis: Compare automated detection vs. manual review.
- Long-Term Effects of False Positives: How might mislabeling legitimate users erode trust?
- Advanced Feature Engineering: What additional signals (e.g., content quality, login behavior) could improve detection?
- Scaling and Fairness: How to maintain performance and fairness at very large scale without disproportionate impacts on subgroups?
Solution
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