Explore the vast range of titles available.

Since the COVID-19 outbreak, people have been spending more time in the online world because of restrictions on face-to-face communication due to epidemic prevention controls. This has also brought the issue of Internet addiction, including the overuse and negative effects of short videos, to the forefront of attention. Past research has found that Internet addiction has a negative impact on well-being. However, there is a special concept of positive emotion called “serendipity” (小确幸). Serendipity provides a small, fleeting but positive experience, yet it is often associated with negative perceptions from an outside perspective. However, the relationship between short video addiction and serendipity is not yet known. Based on this, a theoretical model was developed in the context of the I-PACE model. To understand the relationship between short video addiction and serendipity among college students, in this study, we conducted snowball sampling and distributed online questionnaires using the Wenjuanxing platform. The target population of the questionnaire distribution was vocational college students in China, of whom 985 valid study participants responded, yielding a valid return rate of 82.1%. Of the respondents, 410 (41.6%) were male and 575 (58.4%) were female. The results were as follows: a. short video flow had a positive relationship with serendipity, a negative relationship with achievement motivation, and a positive effect on short video addiction; b. short video addiction had a positive effect on serendipity and a negative effect on achievement motivation; and c. serendipity had a negative impact on achievement motivation. This shows that short video addiction, like other Internet addictions, can have a negative impact on students’ learning.

Short-video dependence (SVD) has become a significant mental health issue around the world. The lack of scientific tools to assess SVD hampers further advancement in this area. This study aims to develop and validate a scientific tool to measure SVD levels, ensuring a scientifically determined cutoff point. We initially interviewed 115 highly engaged short-video users aged 15 to 63 years. Based on the summary of the interview and references to the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) criteria for behavioral addictions, we proposed the first version of the short-video dependence scale (SVDS). We then screened the items through item analysis (second version) and extracted common factors using exploratory factor analysis (third version) and confirmatory factor analysis (final version). Convergent validity was tested with other scales (Chinese Internet Addiction Scale [CIAS] and DSM-5). Finally, we tested the validity of the final version in 16,038 subjects and set the diagnostic cutoff point through latent profile analysis and receiver operating characteristic curve analysis. The final version of the SVDS contained 20 items and 4 dimensions, which showed strong structural validity (Kaiser-Meyer-Olkin value=0.94) and internal consistency (Cronbach α=.93), and good convergent validity (r =0.61 and r =0.68), sensitivity (0.77, 0.83, 0.87, and 0.62 for each of the 4 dimensions), and specificity (0.75, 0.87, 0.80, and 0.79 for each of the 4 dimensions). Additionally, an SVDS score of 58 was determined as the best cutoff score, and latent profile analysis identified a 5-class model for SVD. We developed a tool to measure SVD levels and established a threshold to differentiate dependent users from highly engaged nondependent users. The findings provide opportunities for further research on the impacts of short-video use.

As short video addiction has gradually become an emerging Internet behavioral addiction, its negative impacts on the student population have been noticed and cannot be ignored. Based on a literature review, this study referred to the person-process-content framework and drew on the ecosystem theory to define the relationship between short video addiction, learning avoidance motivation, learning commitment, and silent classroom behavior by using structural equation modeling. This study recruited 1000 participants from Chinese vocational colleges to fill out questionnaires. With an effective recovery rate of 94.6%, there were 946 effective study participants comprising 445 males (47%) and 501 females (53%). After it was confirmed that the data passed the reliability and validity tests, structural equation model analysis was carried out. The study results showed that short video addiction was positively correlated with learning avoidance motivation, but negatively correlated with learning commitment; on the other hand, study avoidance motivation was negatively correlated with learning commitment, but positively correlated with silent classroom behavior. Meanwhile, there was a negative correlation between learning commitment and silent classroom behavior. As a result, the negative correlation between short video addiction and learning behavior needs attention from parents and teachers.

Background Melasma is a common chronic hyperpigmentation disorder that substantially impairs patients' quality of life. With the rapid growth of short‐video platforms such as TikTok and Bilibili, an increasing number of patients are turning to these media for health‐related information. This study aimed to evaluate the quality and reliability of melasma‐related videos available on TikTok and Bilibili. Methods Between August 17 and 19, 2025, we searched Douyin (the Chinese version of TikTok) and Bilibili using the Chinese keyword “黄褐斑” (“melasma”), and included the top 150 videos under each platform's default comprehensive ranking. The search and analysis were conducted in Chinese, reflecting the linguistic and geographical context of mainland China. Video characteristics and engagement metrics were recorded. The quality and reliability of the videos were independently evaluated by two researchers using the Global Quality Score (GQS) and the modified DISCERN (mDISCERN) instrument. Results A total of 237 videos were included in this study. Content was dominated by clinical manifestations (46.8%), etiology (44.3%), and diagnosis (40.1%), whereas treatment‐related content was markedly underrepresented (9.7%). The median video length was 127.00 s (70.75–270.50) on Bilibili and 47.00 s (35.00–96.00) on TikTok. TikTok videos achieved significantly higher engagement than Bilibili (p < 0.05). Overall video quality was moderate, with both GQS and mDISCERN showing a median score of 3.00 (IQR: 2.00–4.00). The mDISCERN score of Bilibili videos was 3.00 (3.00–4.00), significantly higher than TikTok (p < 0.05). Videos uploaded by healthcare professionals scored 3.00 (3.00–4.00) on GQS and 3.00 (2.00–4.00) on mDISCERN, both significantly higher than those uploaded by non‐healthcare professionals (p < 0.05). Conclusions This study found that melasma‐related short videos presented an incomplete content structure, with treatment‐related information being markedly underrepresented. The overall quality of the videos was moderate, whereas those produced by healthcare professionals demonstrated higher quality and reliability. Future efforts should encourage greater participation from healthcare professionals and the implementation of refined content strategies, with the aim of improving both the quality and educational value of dermatology‐related short video resources.

Through a variety of formats, short-form videos evoke emotions in their target audiences to serve specific political objectives. The Communist Party of China (CPC) exemplifies this through its softer propaganda approach that seeks to reach audiences emotionally. At the same time, there is little research on audience interpretations and emotional responses to this emotional propaganda or on their broader attitude toward the propaganda system. This study addresses the existing research gap by conducting semi-structured, in-depth interviews with 32 Chinese citizens and employing folk theories to uncover their emotional responses and associated outcomes. The findings reveal that, other than the conformity exemplified by feeling touched emotionally, two additional emotional stances of neutral indifference and aversion have emerged that represent moderate and strong facets of emotional resistance. Based on these folk theories, this study delineates audience–authority relationships into co-construction, compromised adaptation, and opposition. Finally, this paper integrates individual, political, cultural, and technological factors to explain the reasons behind each type of emotional response. By offering theoretical and practical insights, this research illuminates the tensions and challenges associated with emotional manipulation and the management of audience emotions within an authoritarian context.

Background Problematic short-video use (PSVU) is prevalent among adolescents and is increasingly recognized for its association with adverse mental health outcomes. This cross-sectional study aims to explore the association between PSVU and depression using SFPSVUS, and to develop an interpretable machine learning (ML) model for identifying adolescent depression. Methods A cross-sectional analysis was conducted on data from 4000 adolescent participants. Multivariate Logistic Regression and restricted cubic spline (RCS) methods were used to assess the association between PSVU and depression. Feature variables were identified via Boruta feature selection and validated using Shapley Additive Interpretation (SHAP) methods. Nine ML models, including XGBoost, support vector machine (SVM), and random forest (RF), were developed to identify adolescents with depression. Results A total of 3816 adolescents were included. The SFPSVUS demonstrated acceptable reliability (Cronbach’s α = 0.70) in the current sample. Multivariate Logistic Regression analysis revealed a significant positive correlation between PSVU and depression. RCS analysis indicated a significant nonlinear association between PSVU and adolescent depression. Six variables selected via Boruta feature selection were used to develop nine ML models, with the RF model demonstrating superior performance in identifying depression (AUC = 0.863) in the test set. PSVU emerged as an important feature in the model, alongside emotion regulation, non-suicidal self-injury (NSSI) and sleep quality. Conclusions PSVU shows a significant positive association with adolescent depression and serves as an important feature in ML identification models. Clinical trial number Not applicable.

Short videos are very popular among students, but the immersive nature of the software makes them prone to problematic use and even addiction. Internet addiction, including short-video problematic use, has been a hot topic in recent years due to the COVID-19 epidemic. However, there are few empirical studies that have explored the effects of short-video problematic use on students. Thus, vocational colleges in China were recruited to participate in this study. There were 1089 effective participants, with a valid recall rate of 90.8%. This included 466 male students (42.8%) and 623 female students (57.2%), with a mean age of 19.19 years (standard deviation of 1.07 years). Five hypotheses were proposed and validated by structural equation modeling within the framework of ecological systems theory and engagement theory to explore the association of short-video problematic use, three types of learning engagement, and perceived learning ineffectiveness. Research findings showed that: (1) short-video problematic use has a negative effect on behavioral engagement; (2) behavioral engagement has a positive effect on both emotional and cognitive engagement; and (3) emotional and cognitive engagement have a negative effect on perceived learning ineffectiveness. According to the results, it can be seen that short-video problematic use has a detrimental effect on students’ learning experiences, so teachers and parents need to pay attention to the negative effects of excessive use among students.

With the rising number of “three rural” short videos, various problems faced in the process of increasing their dissemination influence have gradually come to the fore. This paper proposes a multivariate linear regression model based on the dissemination of “three rural” short videos. Determine the research problem, select the three most significant fan numbers of governmental TikTok numbers of the short video platform as the research object, and the author through the set of searchers, analysts, and other crawler software to obtain the research of the initial data. The purpose of the study was to determine the dependent variable and dependent variable. At the same time, the initial data of the study was tested for reliability, and multiple linear regression was used to empirically analyze the communication of short videos of three rural areas from the new media perspective. The results show that narrative style is significant (p<0.01) for communication effect (0.0015**), communication breadth (0.0018**), communication identity (0.0011**), and communication participation (0.0007**), indicating that different narrative styles have differences in communication effect, communication breadth, communication identity, and communication participation. The regression coefficients of non-presenter narratives in the three dimensions of communication breadth, communication identity, and communication participation are −1.213 (p<0.05), −1.336 (p<0.01), and −1.623 (p<0.01), all of which have a significant negative impact on non-participatory narratives. The findings of this paper have particular theoretical significance. They can provide empirical support for the study of the factors influencing the communication effects of short videos in agriculture-related and other categories.

This paper proposes the MT-DQN model, which integrates a Transformer, Temporal Graph Neural Network (TGNN), and Deep Q-Network (DQN) to address the challenges of predicting user behavior and optimizing recommendation strategies in short-video environments. Experiments demonstrated that MT-DQN consistently outperforms traditional concatenated models, such as Concat-Modal, achieving an average F1-score improvement of 10.97% and an average NDCG@5 improvement of 8.3%. Compared to the classic reinforcement learning model Vanilla-DQN, MT-DQN reduces MSE by 34.8% and MAE by 26.5%. Nonetheless, the authors also recognize challenges in deploying MT-DQN in real-world scenarios, such as its computational cost and latency sensitivity during online inference, which will be addressed through future architectural optimization.

Purpose This study aims to explore the switching behaviour of short video (SV) users and its influencing factors and promote the sustainable development of SV platforms (SVPs) and the marketing strategy formulation of library and information institutions. Design/methodology/approach Using the qualitative research method of semi-structured interviews and grounded theory, this study conducts an exploratory study on the user switching phenomenon of an SVP. The authors encoded the interview text at three levels, extracted the factors influencing user switching behaviour on an SVP and constructed the corresponding theoretical model. Findings This study identifies the following major internal and external factors influencing user switching behaviour of SVP: platform quality, social environment, individual characteristics and use needs. It also elaborates on the impact of these internal and external factors on user switching behaviour. Originality/value This study explored the factors influencing SV user switching behaviour and constructed corresponding theoretical models, enriching research in information technology and social media switching. In practice, this study helped the existing SVPs and library and information institutions establish a corresponding early warning mechanism to prevent the loss of existing users and attract new users.