I went straight into solutions mode and that was probably the wrong call.
Start by defining the problem clearly, including the types of fake news and their impact on users and the platform. Then, propose a product solution that balances user engagement, platform integrity, and scalability, considering both proactive and reactive measures. Finally, outline how you would measure success and iterate based on metrics and user feedback.
Pro tip: Emphasize the importance of cross-functional collaboration with policy, legal, and engineering teams to ensure the solution is feasible and compliant. Also, highlight the need to balance false positives and false negatives to avoid over-censorship and maintain user trust.
Clarify what constitutes fake news (misinformation, disinformation, malinformation) and its impact on Facebook's ecosystem, including user trust, engagement, and societal harm.
Analyze why fake news spreads (e.g., algorithmic amplification, lack of media literacy, echo chambers) and understand user behaviors and motivations behind sharing and consuming content.
Generate ideas for both proactive (e.g., AI detection, fact-checking partnerships) and reactive (e.g., labeling, reducing distribution) measures, considering trade-offs between accuracy, speed, and user experience.
Select the most impactful and feasible features for an initial launch, such as a combination of AI flagging and third-party fact-checking, and define clear success metrics (e.g., reduction in shares of false content).
Outline how you would measure effectiveness (e.g., A/B tests, user surveys) and iterate based on data, while addressing potential unintended consequences like false positives or reduced engagement.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.