Animated short video viewing rough behavior with stochastic rough decision modeling

Abstract

China’s short video platform has shown rapid development in terms of user scale and its industry since 2015. As of June 2021, the number of short video users has reached 888 million(China Internet Network Information Center, 2021), an increase of 14.4 million compared with December 2020, accounting for 87.8% of the overall. Animated short videos have a subtle effect and influence on the healthy growth of young minds. As some animated short videos containing harmful information such as violence, pornography and vulgarity are spreading across social media through mobile Internet, they induce some adolescents to watch them obsessively and form addictive behaviors, which seriously endanger the physical and mental health of adolescents. Therefore, this study aims to reduce the unsafe behaviors of adolescents watching short animation videos based on the theory of planned behavior and S-Rough set theory. From the perspective of the Theory of Planned Behavior, the paper investigates the main factors and decision-making mechanisms that influence viewing behavior. To address the limitations of the Theory of Planned Behavior, S-Rough set theory is used to explore users’ uncertain subjective crude behaviors due to individualized needs and random emotions, identify the key attributes that influence users’ decisions, and construct a crude behavior model of animated short video viewing. The Rough behavior model is constructed by using the equivalence classes of SC (Subjective Choice) set X and CI (Choice Intention) set Y  and， establishing a Rough law analysis method to study the interrelationship between SC and CI, propose a random decision method for user animation viewing behavior. The results of this study show that Rough set theory can be applied to the study of random and individualized behavior. The research results show that the theory in analyzing the decision of animated short video viewing behavior is a way, which can provide new ideas for in-depth analysis of animated short video viewing behavior and provide targeted suggestions and strategies for animated short video development, recommendation, and users’ safe viewing.