Fraud-Proof Revenue Division on Subscription Platforms

TL;DR

This paper proposes a new revenue division mechanism for subscription platforms that inherently resists manipulation, outperforming existing methods in fairness and computational tractability.

Contribution

It introduces the ScaledUserProp rule satisfying all manipulation-resistance axioms, addressing limitations of current fraud detection approaches.

Findings

Existing rules can be manipulated and are computationally intractable to detect fraud.

The proposed ScaledUserProp rule satisfies all manipulation-resistance axioms.

Experiments show ScaledUserProp is fairer and more robust than current methods.

Abstract

We study a model of subscription-based platforms where users pay a fixed fee for unlimited access to content, and creators receive a share of the revenue. Existing approaches to detecting fraud predominantly rely on machine learning methods, engaging in an ongoing arms race with bad actors. We explore revenue division mechanisms that inherently disincentivize manipulation. We formalize three types of manipulation-resistance axioms and examine which existing rules satisfy these. We show that a mechanism widely used by streaming platforms, not only fails to prevent fraud, but also makes detecting manipulation computationally intractable. We also introduce a novel rule, ScaledUserProp, that satisfies all three manipulation-resistance axioms. Finally, experiments with both real-world and synthetic streaming data support ScaledUserProp as a fairer alternative compared to existing rules.