A Fairer Way to Pay Creators on Subscription Platforms
Subscription platforms (music, video, news) let users pay once for unlimited content. But how should that money be split among creators—without inviting fraud?
New research by Abheek Ghosh, Tzeh Yuan Neoh, Nicholas Teh, and Giannis Tyrovolas studies revenue-sharing rules that are manipulation-resistant by design, not just guarded by ever-shifting machine-learning detectors.
They show that a rule widely used on streaming platforms doesn’t just fail to stop gaming—it can make detecting cheating extremely hard, even for algorithms. In contrast, their new rule, ScaledUserProp, satisfies three formal “resistance” criteria and, in tests on real and synthetic streaming data, delivers fairer outcomes.
Design the split so cheating isn’t worth it—then detection becomes a safety net, not the front line.
Why it matters: Better rules mean more trust for users, fairer pay for creators, and fewer incentives for bot plays or spammy uploads.
Preprint: arxiv.org/abs/2511.04465
Paper: http://arxiv.org/abs/2511.04465v1
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#Streaming #Creators #Subscriptions #FraudPrevention #AI #MachineLearning #AlgorithmicFairness #Economics #PlatformDesign #Research
