Build a comprehensive decentralized content moderation system for Web3 social platforms that balances free expression with community safety, covering jury-based dispute resolution, stake-weighted reporting, appeal mechanisms, and algorithmic pre-screening with human oversight.
## ROLE You are a decentralized governance architect and content policy specialist who has designed moderation systems for Web3 social platforms, DAOs, and tokenized communities. You understand the fundamental tension in decentralized content moderation — how to protect community standards without creating centralized censorship, why purely algorithmic moderation fails for nuanced cultural content, and how economic incentives can align moderator behavior with community values. Your expertise spans mechanism design for dispute resolution, stake-weighted voting systems, reputation-based jury selection, appeal tribunal architectures, and the legal considerations around content liability in decentralized networks. You have studied the moderation approaches of Lens Protocol, Farcaster, Bluesky, Kleros arbitration, and traditional platform trust and safety teams, synthesizing the strengths of each into frameworks that work without a central authority. ## OBJECTIVE Design a complete decentralized content moderation framework for a [PLATFORM TYPE: social media protocol / NFT marketplace / creator community platform / decentralized forum / messaging network / content publishing platform / gaming social layer / professional networking dApp]. The platform serves approximately [SIZE: 1,000-10,000 / 10,000-100,000 / 100,000-1M / 1M+ users] across [REGIONS: single country / Western markets / global with multi-language / specific cultural context]. The moderation philosophy follows [APPROACH: maximum free expression with minimal intervention / community-standard enforcement similar to Reddit / strict professional standards similar to LinkedIn / creator-friendly with strong anti-harassment focus / child-safety-first with age-gated content tiers]. ## TASK: COMPLETE DECENTRALIZED MODERATION SYSTEM ### Section 1 — Content Policy Architecture Define the content policy framework that establishes what is and is not acceptable on the platform. Unlike centralized platforms where a trust and safety team writes a monolithic policy document, a decentralized framework requires policies that are machine-readable, version-controlled on-chain, and amendable through governance. Design [NUMBER: 3-5] policy tiers: Tier 1 covers universally prohibited content that is illegal in most jurisdictions and non-negotiable regardless of community vote — child sexual abuse material, direct terrorism recruitment, doxxing with intent to harm, and non-consensual intimate imagery. Tier 2 covers community-defined standards that are established through governance proposals and may vary between sub-communities — hate speech thresholds, NSFW content rules, spam definitions, and misinformation policies. Tier 3 covers contextual moderation that depends on the specific community or channel — off-topic content, low-effort posting, promotional content, and community-specific cultural norms. For each tier, specify the enforcement mechanism: Tier 1 uses automated detection plus mandatory human review with zero tolerance, Tier 2 uses community jury review with defined penalties, and Tier 3 uses local moderator discretion with community appeal rights. Define how policies are proposed, debated, and ratified through the governance system — what quorum is required to change a Tier 2 policy, who can propose changes, and what safeguards prevent the community from voting to allow harmful content. ### Section 2 — Reporting & Detection System Design the reporting infrastructure that identifies potentially violating content. The reporting system should have multiple input channels: user reports filed by community members who flag content with a category selection and optional explanation, automated detection systems that use AI classifiers to pre-screen content for Tier 1 violations and flag borderline Tier 2 content for human review, and proactive monitoring by elected community moderators who patrol high-traffic areas. For user reports, implement a stake-weighted reporting mechanism — reporters must stake [AMOUNT: a small token amount] when filing a report, which is returned if the report is upheld and forfeited if the report is found to be frivolous or malicious. This economic skin-in-the-game prevents report spam and weaponized mass-reporting while still keeping reporting accessible through [MECHANISM: free reports for new users up to a limit / micro-stakes starting at the equivalent of $0.01 / reputation-based reporting where trusted reporters stake less]. Design the report prioritization algorithm that considers: severity of alleged violation (Tier 1 gets immediate attention), reporter reputation score (historically accurate reporters get priority), engagement level on the flagged content (viral content needs faster review), and the number of unique reporters flagging the same content. Specify the automated detection stack — which AI models are used, how they are trained on the platform's specific content policies, how false positive rates are monitored and minimized, and how the community can audit the detection algorithms to prevent bias. ### Section 3 — Jury Selection & Deliberation Protocol Design the decentralized jury system that replaces centralized moderator decisions with community-driven content review. Define the jury pool — who is eligible to serve as a juror. Eligibility criteria should include: minimum platform tenure of [DURATION: 30 / 60 / 90 days], minimum reputation score earned through positive community participation, completion of a moderation training module that covers content policies and cultural sensitivity, and staking of [AMOUNT: tokens] as commitment to honest deliberation. Design the jury selection algorithm that ensures fairness: random selection from the eligible pool weighted by reputation score, stratified sampling to ensure geographic and cultural diversity on the jury, conflict-of-interest screening that excludes jurors who have interacted with the reported content or its creator, and panel sizing based on case severity — [NUMBER: 3] jurors for routine Tier 3 cases, [NUMBER: 7] jurors for Tier 2 cases, and [NUMBER: 11-15] jurors for Tier 1 cases or appeals. Specify the deliberation process: jurors review the content independently without seeing other jurors' opinions (to prevent anchoring bias), submit their verdict with a written rationale, and then enter a structured discussion phase if the initial vote is not unanimous. Define the verdict thresholds — what majority is required to uphold a report (simple majority, two-thirds, or unanimous depending on severity), what happens in case of a tie, and how dissenting opinions are recorded for appeal consideration. ### Section 4 — Penalties & Enforcement Mechanisms Design the penalty framework that applies graduated consequences for content violations. Define [NUMBER: 5-7] penalty levels: Level 1 is a content label or warning that adds context to the content without removing it (similar to Twitter community notes), Level 2 is content visibility reduction where the content is de-amplified in feeds and recommendations but remains accessible via direct link, Level 3 is content removal where the content is hidden from the platform interface but the on-chain record remains as an immutable audit trail, Level 4 is a temporary account restriction where the user loses posting privileges for [DURATION: 24 hours / 7 days / 30 days] depending on violation severity and history, Level 5 is a permanent account flag that marks the account as a repeat offender with restricted functionality across the protocol, and Level 6 is protocol-level blocking where the account is blacklisted at the smart contract level for Tier 1 violations. Specify how penalties escalate for repeat offenders — first offense receives a warning, second offense within [PERIOD: 90 days] receives Level 2 or 3 penalty, third offense triggers automatic Level 4 review, and a pattern of violations triggers community review for Level 5 designation. Include the "penalty appeal" mechanism where penalized users can request a new jury review of their case with an appeal stake that is higher than the original report stake. Design the rehabilitation pathway — how users who have served penalties can restore their reputation through positive community contributions over time. ### Section 5 — Moderator Incentives & Reputation System Design the economic incentive structure that rewards honest, consistent moderation and punishes corruption or negligence. Define the moderator reward system: jurors who participate in case reviews earn [REWARD: token rewards / reputation points / governance power / platform fee share] proportional to the time and complexity of the cases they review. Jurors whose verdicts align with the panel majority receive full rewards, while jurors who are consistently in the minority on clear-cut cases see their reputation score decrease. Implement the "Schelling point" mechanism — jurors are incentivized to vote according to how they believe an honest, policy-following juror would vote, creating a coordination game that rewards genuine assessment over personal bias. Design the reputation score formula that considers: total cases reviewed, verdict accuracy rate (alignment with final outcomes including appeals), response time (prompt jurors are rewarded more than slow ones), written rationale quality (assessed through peer review of reasoning), and diversity of case types reviewed. Include anti-collusion measures — detection algorithms that identify juror cartels who consistently vote as a block, mandatory rotation of jury compositions, and random audits where known test cases with predetermined correct verdicts are inserted into the review queue to calibrate juror accuracy. Specify the moderator election system for elevated roles — community-elected "chief moderators" who handle edge cases, set moderation priorities, and represent the moderation team in governance discussions. These roles should have term limits of [DURATION: 3 / 6 / 12 months] with re-election requirements. ### Section 6 — Appeal Tribunal & Escalation Protocol Design the multi-layer appeal system that ensures no single moderation decision is final without recourse. Level 1 appeal allows the affected user to request a new jury panel review of the original case — the appeal jury must be entirely different from the original jury and receives both the original content and the first jury's reasoning. Level 2 appeal escalates to a specialized tribunal of [NUMBER: 5-7] elected senior moderators with demonstrated expertise in the specific content policy area — these tribunal members review the case, both jury deliberations, and any new evidence submitted by the user. Level 3 appeal is reserved for cases with protocol-wide implications or precedent-setting potential — a full governance vote where the entire token-holding community can weigh in on whether the moderation decision aligns with platform values. Define the appeal filing requirements: users must submit within [DURATION: 7 / 14 / 30 days] of the original decision, must stake [AMOUNT: increasing with each appeal level] to prevent frivolous appeals, and must provide a written argument explaining why the original decision was incorrect. Specify the success rate targets — if more than [PERCENTAGE: 20-30%] of appeals at any level are overturning original decisions, the system is flagging a calibration problem with the initial jury process that needs investigation. Include the "precedent database" that records all appeal outcomes with reasoning, creating a case law repository that future jurors can reference for guidance on edge cases. ### Section 7 — Cross-Platform Interoperability & Legal Compliance Address how the decentralized moderation system interacts with external platforms and legal requirements. Design the shared blocklist protocol that allows different platforms building on the same decentralized social protocol to share moderation data — when one platform identifies a Tier 1 violation, other platforms can automatically apply their own policies to the same content or account. Specify the opt-in framework — each platform chooses which shared moderation signals to consume based on their own policy alignment, preventing a single platform's moderation decisions from unilaterally censoring content across the network. Address legal compliance requirements: how the system handles law enforcement requests for content removal, how DMCA or equivalent copyright takedowns integrate with the jury process, how GDPR right-to-erasure requests interact with immutable blockchain records (using encryption key deletion or off-chain content storage with on-chain access pointers), and how geographic content restrictions apply when the platform operates globally but certain content is illegal in specific jurisdictions. Include the regulatory risk assessment — which aspects of the decentralized moderation system might face legal challenges, how the system can adapt to evolving digital content regulations, and how liability is distributed across protocol operators, node runners, moderators, and users.
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