How would you evaluate stolen-post detection?

Q: How would you evaluate stolen-post detection?

This question evaluates product analytics, experimental design, and causal thinking for content-moderation algorithms—specifically metric specification, trade-off/harm analysis, and online experiment logistics—and is commonly asked to gauge a data scientist’s ability to balance detection accuracy, stakeholder impacts, and business objectives in production features; it is in the Analytics & Experimentation category for a Data Scientist position. At a high abstraction level it probes system-level reasoning around problem scoping, failure modes, metric frameworks, A/B or quasi-experiment setup, and post-launch monitoring without requiring implementation-level detail.

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Analytics & Experimentation questions require understanding of core concepts and practice. PracHub provides solutions with explanations to help you master analytics & experimentation interviews.

Question

You are interviewing for a Meta DSA (product analytics / data science) role. The product team is launching a new Stolen Post Detection algorithm that flags posts suspected of being copied/reposted without attribution, and then triggers actions (e.g., downrank, warning label, creator notification, or removal).

Design an evaluation plan covering:

Problem diagnosis & clarification: What questions would you ask to clarify the product goal and the meaning of “stolen” (e.g., exact duplicate vs paraphrase vs meme templates), enforcement actions, and success criteria?
Harms & tradeoffs: Enumerate likely failure modes and harms of false positives vs false negatives, including different stakeholder impacts (original creator, reposter, viewers, moderators).
Metrics: Propose a metric framework with (a) primary success metrics, (b) guardrails, and (c) offline model metrics. Include at least one metric that can move in opposite directions depending on threshold choice.
Experiment design: Propose an online experiment (or quasi-experiment if A/B is hard). Address logging, unit of randomization, interference/network effects, ramp strategy, and how you would compute/think about power/MDE.
Post-launch monitoring: What would you monitor to detect regressions or gaming, and how would you iterate on thresholds/policy over time?

How would you evaluate stolen-post detection?

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