Sure, let’s break it down into three steps: gather metadata, apply anomaly scoring, then push a flag. First, scrape the accounts that hit the post in bulk and record their creation date, follower count, activity pattern, and content similarity. Second, run a simple heuristic—if an account was created within the last week, has fewer than 50 followers, and posts identical text to other flagged users, give it a high anomaly score. Finally, feed the high‑score accounts into a moderation queue or an automated block list. That keeps the process transparent and repeatable, and you can audit the rules later if something slips through.

Good point on the edge cases—add a “review if score > 80 but total engagement > 200” rule so we don’t drown real fans in false positives. And for scraping, switch to incremental pulls or use a public API when possible; that cuts bandwidth and keeps the system lean. Let’s deploy the flags and watch the logs for any misfires. If bots slip through, we’ll tweak the thresholds—nothing’s perfect at first pass.

Sounds like a plan. Deploy the rules, monitor the logs, and adjust the thresholds if any real fans get flagged. We’ll keep it lean and transparent, so the bandwidth stays under control. Good to have a systematic loop in place.