I went straight to engagement metrics and then realized mid-answer that was probably the wrong instinct for a trust and safety problem.
Start by clarifying the goal of reducing misinformation—whether it's reducing exposure, sharing, or belief in false content—and then define a multi-layered metric framework that captures prevalence, user engagement, and ecosystem health. Emphasize a balanced approach that considers both intended outcomes and potential unintended consequences, using A/B tests and holdout groups to measure causal impact.
Pro tip: Acknowledge the trade-off between reducing misinformation and maintaining user engagement, and propose guardrail metrics to ensure that efforts don't inadvertently suppress legitimate speech or harm user experience.
Clarify what 'misinformation' means in this context and what specific behaviors or outcomes you aim to change (e.g., reduce exposure, sharing, or belief). Align success criteria with Meta's broader goals of fostering a safe and informed community.
Choose metrics across different layers: prevalence (e.g., % of misinformation in feed), engagement (e.g., shares, comments), user perception (e.g., survey-based trust), and ecosystem health (e.g., reporting rates). Include both leading and lagging indicators.
Use A/B tests or holdout groups to measure causal impact of initiatives. Define control and treatment groups, randomization unit, and duration. Consider long-term effects and novelty effects.
Evaluate primary metrics against guardrails (e.g., user engagement, false positives). Segment by user demographics and content types to uncover heterogeneous effects. Use statistical significance and practical significance.
Based on results, recommend iterations or scaling. Communicate findings to stakeholders, emphasizing trade-offs and learnings. Establish a continuous monitoring system for ongoing assessment.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.