Fake Accounts [AE]

# Detecting and Managing Bad Accounts on a Social Platform ## 1) Probability of a Bad Account Sending Friend Requests Context: 1% of accounts are bad. Bad accounts send friend requests at 10× the rate of good accounts. - If a user receives one friend request, what is the probability it comes from a bad account? - If a user receives five friend requests, what is the probability at least one is from a bad account? ## 2) Classification Model Performance We build a classifier to detect bad accounts. It achieves a true positive rate (TPR) of 95% and a true negative rate (TNR) of 95%. - If the model predicts an account is bad, what is the probability the account is actually bad? ## 3) Feature Engineering for Bad Account Detection What types of data would you use to determine whether an account should be classified as a bad or good account? ## 4) Assessing the Bad Account Problem How would you determine whether bad accounts pose a significant issue to the platform? Would you use stratified sampling, random sampling, or another approach? ## 5) Defining a Bad User How would you define a “bad user” in the context of a social media platform? ## 6) Impact of Fraudulent Users What are the potential impacts of fraudulent or bad users on the platform and its community? ## 7) Impact of Friend Requests from Bad Accounts What potential effects might arise from friend requests initiated by bad accounts? ## 8) Precision–Recall Tradeoff When building a machine learning model to identify bad accounts, how would you approach the tradeoff between precision and recall? In which situations would you prioritize one over the other?