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

• URL: http://arxiv.org/abs/2601.15975v1
• Date: Thu, 22 Jan 2026 13:54:06 GMT
• Title: Unveiling and Simulating Short-Video Addiction Behaviors via Economic Addiction Theory
• Authors: Chen Xu, Zhipeng Yi, Ruizi Wang, Wenjie Wang, Jun Xu, Maarten de Rijke,
• Abstract summary: Short-video platforms have large-scale behavioral data, offering a valuable foundation for analyzing addictive behaviors.<n>Our analysis shows that short-video addiction follows functional patterns similar to traditional forms of addictive behavior.<n>To develop a simulator that can learn and model these patterns, we introduce a novel training framework, AddictSim.
• Score: 53.46648619584349
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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