Bulk-moderation: mass-block users who liked a post via automated filters

Many professional and semi-professional creators and community managers — roughly 12 million people in the estimated TAM — regularly endure waves of harassment, coordinated liking by bot networks, and monetization risk when abusive accounts engage with their posts, yet current moderation workflows are manual, ad-hoc, and time-consuming. The problem is acute because a single viral post can attract thousands of low-quality or coordinated likes that teams need to neutralize quickly without over-blocking legitimate fans. You could build "Bulk-moderation," a cross-platform SaaS that ingests like-engagement lists, scores accounts with explainable ML signals (bot probability, coordination metrics, account age, engagement patterns), and applies user-configurable automated filters to bulk-block, mute, or flag accounts with preview/undo, role-based approvals, and audit logs. Revenue can follow the $100 ARPU/year assumption across 12M potential users (a $1.2B TAM) via tiered subscriptions, enterprise seats, and integration add-ons, but you must contend with platform API limits, evolving policy constraints, and the challenge of keeping false positives acceptably low (targeting under 2% is ambitious). This opportunity is timely: the market score is 92/100, creators and brands are increasing safety budgets, AI signal enrichment is improving detection accuracy, and moderators want unified cross-platform tooling. To stand out in a medium-competition landscape you should focus on explainability, verifiable audit trails, low false-positive rates, and rapid pilot partnerships (for example, a 100-creator proof cohort), while being candid that platform cooperation and ongoing model maintenance will be the primary operational hurdles.