Detecting Coordinated Reddit Accounts with RedLens

Some Reddit “grassroots enthusiasm” is exactly what it looks like. Some of it is a handful of accounts taking turns seeding threads, upvoting each other, and name-dropping the same product in adjacent subreddits. Telling the two apart by eye is hard; telling them apart from data is mostly mechanical. Here’s the RedLens workflow.

Step 1: Track the topic, not just one account

redlens track "topic or subreddit"

Coordination is a relationship between accounts, so you need the surrounding discussion, not one user’s history. track archives the topic’s threads across its subreddit network into your local SQLite file, incrementally, the same way sync archives a single user.

Step 2: Let the network tools do the counting

redlens leads
redlens brands
redlens seeding

• leads surfaces the accounts doing the heavy lifting in the tracked discussion — who starts threads, who reliably shows up in them.
• brands computes brand-mention metrics: which accounts mention a name, how often, and how concentrated those mentions are in a few voices.
• seeding looks for the tell-tale pattern of coordinated campaigns: the same small set of accounts repeatedly introducing a topic into fresh threads or subreddits.

The coordinated-network dashboard (redlens serve) puts this in a matrix view: accounts × threads, with the lockstep clusters visible as dense blocks instead of the diffuse scatter organic interest produces.

Step 3: Read it conservatively

Co-activity is evidence, not proof. Two accounts that post in the same threads at the same times might be sockpuppets — or coworkers, or fans of the same niche podcast. What makes the case is convergence across independent signals: timing alignment and mention concentration and seeding behavior from accounts with little other history. RedLens gives you each signal separately precisely so you can weigh them instead of trusting one score.

Step 4: Share findings without doxxing anyone

When you write up results, generate the report with pseudonymization on: usernames become stable placeholders, so reviewers can verify the pattern (“account A seeded 9 of 11 threads”) without the write-up becoming a harassment target list. The raw mapping stays in your local database — one SQLite file you control.

All of it runs locally: no API keys, no cloud service seeing your queries, and an archive that still exists if the accounts delete themselves tomorrow — which, in this line of research, they often do.