I’ve been managing campaigns across US and Russian markets for a while now, and honestly, the fraud problem is getting worse. It’s not just inflated followers anymore—it’s sophisticated stuff. Fake engagement patterns, bot networks that look almost human, and the worst part? Different fraud signals in each market.
Last quarter we almost green-lit a campaign with an influencer who looked great on paper. Strong engagement rate, good audience demographics, all the metrics lined up. But when we dug deeper into the actual engagement patterns across both markets—US comments, Russian retweets, interaction timing—something felt off. The engagement was too perfectly distributed. We killed it before launch.
That’s when I realized we need a smarter way to do due diligence. We can’t just rely on surface-level metrics anymore. The bilingual angle is critical too—fraud signals in English markets don’t always translate to Russian markets, and vice versa. A bot farm optimized for US TikTok looks different from one targeting Russian Instagram.
I’m curious how others are handling this. Are you using any specific signals or frameworks to flag risky influencers before committing budget? How do you balance automated checks with human judgment, especially when working across markets with different content norms and engagement patterns?
Exactly the problem I’m seeing too. We’ve built a basic checklist—follower growth velocity, engagement authenticity scores, audience location mismatches—but it’s becoming a bottleneck. With bilingual campaigns, we’re essentially doing due diligence twice, and half the time we’re catching the same red flags we caught in week one.
What we’ve started doing is layering signals. Instead of trusting one metric, we cross-reference engagement patterns, comment sentiment, posting consistency, and audience overlap with known fraud networks. It’s not foolproof, but it’s caught two major fraud attempts in the last six months that our initial screening missed.
The real challenge though? Scaling this without hiring an army of analysts. We’re exploring automation, but I’m hesitant to rely fully on algorithms when the stakes are high. How are you handling the scaling problem?
You’re touching on something critical here—the signal-to-noise problem in cross-market fraud detection. At scale, we deal with thousands of influencers quarterly, and manual due diligence isn’t an option.
Here’s what we’ve learned: single-metric fraud detection fails. You need a composite risk score that weighs multiple signals with market-specific baselines. For US creators, we look hard at follower acquisition speed, bot engagement patterns, and audience location clustering. For Russian market influencers, we adjust those thresholds significantly because engagement norms are genuinely different.
The bilingual angle adds complexity because you’re essentially running two separate risk models and trying to find common ground. We’ve found that the true fraud signals—the ones that matter across both markets—tend to be timing-based: artificial spikes in engagement, coordinated comment attacks, follower drops followed by rapid rebuilds.
But here’s the uncomfortable truth: no algorithm catches everything. You still need human review on influencers above a certain spend threshold. For us, that’s anyone handling more than $10K per campaign. The algorithm narrows the field, but final approval is human-driven.
What’s your current false positive rate? That’s usually where the real cost hides.