feat(web): Wave 4 — prose layouts + /policies on Tailwind typography
diff --git a/content/posts/2026/fake-follower-detection/index.md b/content/posts/2026/fake-follower-detection/index.md new file mode 100644 index 0000000..dcea995 --- /dev/null +++ b/content/posts/2026/fake-follower-detection/index.md @@ -0,0 +1,54 @@ +--- +title: "Fake follower detection" +pubDate: 2026-04-05T20:05:10.000Z +updatedDate: 2026-04-05T20:05:10.000Z +draft: false +excerpt: "Fake followers are the counterfeit currency of the nightlife scene. A DJ with 10,000 followers who can actually bring 20 people to your event is worth less than a DJ … Read more" +categories: + - Uncategorized +featured: + src: https://cdn.slist.net/posts/fake-follower-detection/cover.png + alt: "Magnifying glass over smartphone showing social profiles in dark light" +legacy_wp_id: 16100 +--- +Fake followers are the counterfeit currency of the nightlife scene. A DJ with 10,000 followers who can actually bring 20 people to your event is worth less than a DJ with 800 followers who can fill a room. Here is how we built a vetting system that separates real influence from purchased metrics. + +## Why it matters for promoters + +When you give a DJ a promo code and expect them to drive ticket sales through their following, the quality of that following determines your commission payout and the actual bodies on the dancefloor. A DJ with 5,000 real followers will outsell a DJ with 50,000 purchased followers every time. The fake followers do not buy tickets, do not share stories, and do not show up. + +Promoters who evaluate SLIST by the quality of our resharing accounts required us to vet the accounts we partnered with. One fake account in our promotional network undermines credibility with every partner who sees the inflated engagement metrics. + +## The tools we used + +Inbeat.co was the primary tool for checking follower authenticity before including accounts in promotions. The analysis breaks down follower quality: percentage of real accounts, engagement rate relative to follower count, follower growth patterns, and geographic distribution of the audience.Diff truncated (61 lines total). View full commit on GitHub →
