PM Interview: "How would you improve Twitter's content moderation?"

Tests awareness of content safety versus free expression, automation, abuse prevention, and user trust.

• Breaks down moderation types (automated, human review, crowdsourced)
• Weighs free expression vs. harm mitigation
• Suggests flows for appeals and transparency
• Anticipates loopholes or abuse vectors
• Defines success metrics (content removals, user trust, appeal rates)

• Content policies must balance key values
• Transparency and appeal flow builds trust
• Metrics: time to action, false positives, trust