Unveiling and Simulating Short-Video Addiction Behaviors via Economic Addiction Theory

2026-01-22

AI Summary

This work addresses the challenge of large-scale identification and simulation of user addiction behaviors on short-video platforms by proposing AddictSim, a novel framework that integrates economic addiction theory with large-scale implicit behavioral data from recommender systems. AddictSim captures individualized addiction patterns through personalized modeling and incorporates a diversity-aware mechanism to mitigate addictive engagement. During training, it employs a population-based mean-adaptive strategy grounded in relative policy optimization. Experiments on two real-world large-scale datasets demonstrate that AddictSim effectively simulates addiction dynamics consistent with behavioral economic principles and significantly outperforms existing methods, reducing user addiction intensity while maintaining recommendation performance.

Technology Category

• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior
• Search and Optimization: Sampling/Simulation-based Search
• Game Theory and Economic Paradigms: Adversarial Learning

Application Category

• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Economics, Online Markets and Human Computation: Economics and fairness of platforms and recommendation systems
• Web Mining and Content Analysis: Web data generation and simulation

Abstract

Short-video applications have attracted substantial user traffic. However, these platforms also foster problematic usage patterns, commonly referred to as short-video addiction, which pose risks to both user health and the sustainable development of platforms. Prior studies on this issue have primarily relied on questionnaires or volunteer-based data collection, which are often limited by small sample sizes and population biases. In contrast, short-video platforms have large-scale behavioral data, offering a valuable foundation for analyzing addictive behaviors. To examine addiction-aware behavior patterns, we combine economic addiction theory with users'implicit behavior captured by recommendation systems. Our analysis shows that short-video addiction follows functional patterns similar to traditional forms of addictive behavior (e.g., substance abuse) and that its intensity is consistent with findings from previous social science studies. To develop a simulator that can learn and model these patterns, we introduce a novel training framework, AddictSim. To consider the personalized addiction patterns, AddictSim uses a mean-to-adapted strategy with group relative policy optimization training. Experiments on two large-scale datasets show that AddictSim consistently outperforms existing training strategies. Our simulation results show that integrating diversity-aware algorithms can mitigate addictive behaviors well.

Problem

• short-video addiction
• problematic usage
• behavioral data
• addiction patterns
• user health

Innovation

• economic addiction theory
• large-scale behavioral data
• AddictSim
• personalized addiction modeling
• diversity-aware algorithms