As a hedonic technology, short-form videos (SFVs) have attracted numerous users. However, one related problem that merits research is SFV addiction, especially among adolescents due to their immature self-control abilities. Although recent research has discussed the formation process of SFV addiction from psychological needs and the SFV features perspective, scarce attention has been paid to investigating the relationship between stress and SFV addiction, as well as the relationship between SFV addiction and its consequences. Therefore, the purpose of this study is to examine whether school burnout (school stress), social phobia (social stress), and parental phubbing (family stress) trigger SFV addiction based on stress-coping theory and, furthermore, whether SFV addiction leads to low levels of happiness (psychological consequence), parent–child relationship quality (relational consequence), and perseverance (behavioral consequence) among adolescents. The proposed model was tested based on data collected from 242 adolescents from across China under the age of 18 with the experience watching SFVs. A covariance-based structural equation modeling (CB-SEM) method was used for data analysis. The results showed that school burnout and social phobia significantly triggered SFV addiction, which later negatively and significantly influenced adolescents’ happiness, parent–child relationship quality, and perseverance. The study also found that SFV addiction served as a mediator between the drivers and consequences of SFV addiction. This study provides several theoretical implications. First, this study is one of the first to explain adolescents’ SFV addiction from stress-coping perspective, thereby enriching research in the field of SFV addiction. Second, prior research has rarely discussed the impacts of stresses from various environments on addiction behavior in a single study. Therefore, this study contributes to the knowledge of stress-related research in an SFV addiction context. Finally, our study enhances our understanding of the impact of SFV addiction on its consequences, in both an SFV research context and a social media research context.
With the rapid development of hardware and software technology, modern industry has produced a large amount of high-dimensional unlabeled data, such as pictures and videos. As clusters of these data sets may exist in some subspaces, traditional algorithms are no longer applicable. The related algorithms based on sparse subspace can find clusters in the subspace, which solves the problem of high data dimension. However, their clustering process based on only one single feature, which results in their performance being particularly sensitive to this single view. Affected by the integrated algorithm, a large number of multi-view methods began to emerge. These methods improve the clustering performance by integrating the subspace expressions of multiple views, but the problem is that the complementary information of multiple views cannot be fully considered. In addition, the problem of non-uniform distribution in clusters also exists in high-dimensional data sets. In this paper, based on the multi-view subspace clustering method, a clustering algorithm based on tensor low rank expression is proposed to solve the clustering problem of high-dimensional datasets. On the one hand, this paper solves the problem of noise and data corruption by combining the method of 2,1 norms, which transform the optimization problem of solving multi-view subspace expression into a low-rank expression problem of tensor to fully consider the complementarities between views. On the other hand, the proposed scheme solves the problem of non-uniform distribution in clusters in high-dimensional data by combining with the density peak algorithm based on hierarchical strategy. Experiment results show the effectiveness of the algorithm. INDEX TERMS Cluster, density peak algorithm, subspace, complementary information.
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