Automated mass-blocking of users who liked an egregious post (privacy-first)

Community moderators and power users—roughly 6 million people across mainstream, niche and federated networks—regularly face the tedious task of manually removing or blocking cohorts of users who liked or amplified clearly egregious posts, a problem made worse by platform fragmentation and limited moderation tooling. Current workarounds often rely on third-party scraping or manual inspection, raising privacy concerns and creating scalability bottlenecks for small volunteer teams and paid moderators alike. You could build a privacy-first moderation assistant that performs lightweight on-device risk scoring of likers/reactors, surfaces a reviewed list, and enables reversible mass-block/mass-unfollow actions with local audit logs and optional encrypted cross-platform connectors so no third-party servers retain relationship data. The technical approach would combine client-side ML models, human-in-the-loop batching, and integration layers for common APIs and federated protocols to balance automation with moderator control. The market is attractive now: a conservative TAM of $2.4B (6M moderators × $400 ARPU/year), a market score of 92/100 and revenue potential of 88/100 indicate commercial viability, and tailwinds like platform fragmentation, stronger privacy expectations, and advances in on-device ML lower adoption friction. Many moderators already pay for premium community tools, so a well-positioned product could convert users willing to pay for time savings and safer communities. This product can stand out by making privacy guarantees a core differentiator (no cloud scraping), providing explainable on-device classifiers and an easy UX for non-technical moderators, but realistic challenges include varied platform APIs and federation protocols, API rate limits, false positives and legal/policy risks that require careful technical, legal and partnership work to overcome.