Construction of a Gravity Model of Tiktok Short-Video Traffic Driven by Endogenous Motivation

TikTok(Douying), as one of the major short-video platforms in China, operates under a centralized algorithmic distribution mechanism that concentrates video traffic (views) heavily among a small group of top creators, leaving many high-quality creators underexposed. With the growing trend toward decentralized content distribution, understanding the role of user psychology and behavioral drivers in shaping traffic has become a critical research issue. Drawing on Maslow's hierarchy of needs, Freud's personality structure theory, and Katz's uses and gratifications theory, this study proposes the "Intrinsic Motivation–Traffic Gene Gravity Model" to examine the effects of psychological needs, personality traits, and gratifications on short-video traffic potential. Based on 521 valid Douyin user survey responses, structural equation modeling (SEM) and multiple regression were applied to validate the model. Results indicate that: (1) personality traits have a significant positive effect on gratifications (β = 1.316, p < 0.001); (2) psychological needs have a significant positive effect on personality traits (β = 0.929, p < 0.001); (3) the direct effect of psychological needs on gratifications is not significant (β=–0.350, p = 0.068), suggesting the potential presence of a mediating mechanism; and (4) gratification dimensions significantly predict platform behavioral performance (e.g., usage duration, interaction frequency, content diversity) with strong explanatory power. These findings suggest that user psychological variables exert a substantial gravitational pull in the formation of short-video traffic, with personality traits and gratifications serving as key drivers. This research provides theoretical insights based on user psychological profiling to inform algorithm optimization and content strategy development for creators.