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Background
People with suicidal ideation post suicide-related information on social media, and some may choose collective suicide. Sina Weibo is one of the most popular social media platforms in China, and “Zoufan” is one of the largest depression “Tree Holes.” To collect suicide warning information and prevent suicide behaviors, researchers conduc...
Citations
... Weibo content propagation exhibits characteristics such as user behavior context dependency, structural dependency, and real-time dependency, making the prediction of its propagation dynamics a complex problem. An effective model for predicting content propagation dynamics needs to address these challenges [26][27][28]. Traditional content feature modeling methods have not considered text features in content propagation, and text features are crucial factors influencing propagation dynamics. Therefore, the optimization of traditional content feature modeling methods is initiated. ...
This study aims to investigate the dissemination of traditional Chinese music in the digital era and the application of deep learning in predicting the influence of Weibo content propagation. Firstly, the impact of digitization on the dissemination of traditional Chinese music is analyzed. Secondly, based on the Bidirectional Gated Recurrent Unit (Bi-GRU), traditional content feature modeling methods are optimized by introducing content text features. Simultaneously, the Graph Attention Network (GAN) is divided into three steps, allowing it to consider the edge properties of input sequences. The improved content feature modeling, GAN, and multilayer perceptron are integrated to construct a Context-dependent Dynamic Graph Attention Network (C-DGAN). In order to validate the performance of the C-DGAN model, Mean Square Logarithmic Error (MSLE) is used as the evaluation metric in comparative experiments at observation times T=1, 2, 3, and 4 hours. The results indicate that at T=4 hours, C-DGAN achieves an MSLE of 1.854, reducing by at least 0.134 compared to the baseline model, demonstrating superior performance in predicting the scale of Weibo content propagation. Additionally, in comparison with models using different recurrent neural networks, the model employing the Bi-GRU network performs the best. Thus, the proposed C-DGAN model exhibits excellent performance in predicting Weibo content propagation influence. The study findings provide robust support for the study and practice of Weibo content propagation.
... Given the content of the 11 emotional words included in the EWLSRA, we argue that this latent variable as a suicide-related emotional characteristic. Many previous studies have focused on lexical or content analysis for suicide notes or messages posted online by persons with suicidal ideation or suicide attempts, who died by suicide, or were at-risk for suicidal behaviors (10,11,(36)(37)(38). In China, a Chinese suicide dictionary was developed for content analysis using social media materials (Sina Weibo, a popular microblog in mainland China). ...
Background
Emotional disturbance is an important risk factor of suicidal behaviors. To ensure speech emotion recognition (SER), a novel technique to evaluate emotional characteristics of speech, precision in labeling emotional words is a prerequisite. Currently, a list of suicide-related emotional word is absent. The aims of this study were to establish an Emotional Words List for Suicidal Risk Assessment (EWLSRA) and test the reliability and validity of the list in a suicide-related SER task.
Methods
Suicide-related emotion words were nominated and discussed by 10 suicide prevention professionals. Sixty-five tape-recordings of calls to a large psychological support hotline in China were selected to test psychometric characteristics of the EWLSRA.
Results
The results shows that the EWLSRA consists of 11 emotion words which were highly associated with suicide risk scores and suicide attempts. Results of exploratory factor analysis support one-factor model of this list. The Fleiss’ Kappa value of 0.42 indicated good inter-rater reliability of the list. In terms of criteria validities, indices of despair (Spearman ρ = 0.54, P < 0.001), sadness (ρ = 0.37, P = 0.006), helplessness (ρ = 0.45, P = 0.001), and numbness (ρ = 0.35, P = 0.009) were significantly associated with suicidal risk scores. The index of the emotional word of numbness in callers with suicide attempt during the 12-month follow-up was significantly higher than that in callers without suicide attempt during the follow-up (P = 0.049).
Conclusion
This study demonstrated that the EWLSRA has adequate psychometric performance in identifying suicide-related emotional words of recording of hotline callers to a national wide suicide prevention line. This list can be useful for SER in future studies on suicide prevention.
People worldwide have suffered tremendously in terms of their mental health due to years of exposure to stress, anxiety, and the pressures of today's fast-paced lifestyles. The digitization of the data made possible by advances in health-care technology worldwide has allowed for a more precise mapping of the many variations of human biology than was possible before. People's methods of interaction with one another are evolving due to the rapid development of technology. Twitter, Facebook, Telegram, and Instagram have all risen to prominence as platforms where users can openly discuss their innermost thoughts, psyche, and feelings with one another. Texts are put through a psychological analysis process to pull out relevant details, characteristics, and user feedback. Psychological analysts rely on social media for the early identification of depressive symptoms and patterns of behavior. Machine learning has been recognized as a powerful method for sifting through the vast quantities of data in the health-care industry. Predicting the likelihood of mental diseases and executing likely treatment outcomes is a common application of ML techniques in mental health. This paper compiles a list of different mental health disorders along with the methods used in detecting and diagnosing mental health-related issues using online social media.
Purpose
This study aimed to systematically evaluate the quality of content and information in videos related to gestational diabetes mellitus on Chinese social media platforms.
Methods
The videos on various platforms, TikTok, Bilibili, and Weibo, were searched with the keyword “gestational diabetes mellitus" in Chinese, and the first 50 videos with a comprehensive ranking on each platform were included for subsequent analysis. Characteristic information of video was collected, such as their duration, number of days online, number of likes, comments, and number of shares. DISCREN, JAMA (The Journal of the American Medical Association) Benchmark Criteria, and GQS (Global Quality Scores) were used to assess the quality of all videos. Finally, the correlation analysis was performed among video features, video sources, DISCERN scores, and JAMA scores.
Results
Ultimately, 135 videos were included in this study. The mean DISCERN total score was 31.84 ± 7.85, the mean JAMA score was 2.33 ± 0.72, and the mean GQS was 2.00 ± 0.40. Most of the videos (52.6%) were uploaded by independent medical professionals, and videos uploaded by professionals had the shortest duration and time online (P < 0.001). The source of the video was associated with numbers of “likes", “comments", and “shares" for JAMA scores (P < 0.001), but there was no correlation with DISCERN scores. Generally, videos on TikTok with the shortest duration received the most numbers of “likes", “comments", and “shares", but the overall quality of videos on Weibo was higher.
Conclusion
Although the majority of the videos were uploaded by independent medical professionals, the overall quality appeared to be poor. Therefore, more efforts and actions should be taken to improve the quality of videos related to gestational diabetes mellitus.
As psychological diseases become more prevalent and are identified as the leading cause of acquired disability, it is essential to assist people in improving their mental health. Digital therapeutics (DTx) has been widely studied to treat psychological diseases with the advantage of cost savings. Among the techniques of DTx, a conversational agent can interact with patients through natural language dialog and has become the most promising one. However, conversational agents' ability to accurately show emotional support (ES) limits their role in DTx solutions, especially in mental health support. One of the main reasons is that the prediction of emotional support systems does not extract effective information from historical dialog data and only depends on the data derived from one single-turn interaction with users. To address this issue, we propose a novel emotional support conversation agent called the STEF agent that generates more supportive responses based on a thorough view of past emotions. The proposed STEF agent consists of the emotional fusion mechanism and strategy tendency encoder. The emotional fusion mechanism focuses on capturing the subtle emotional changes throughout a conversation. The strategy tendency encoder aims at foreseeing strategy evolution through multi-source interactions and extracting latent strategy semantic embedding. Experimental results on the benchmark dataset ESConv demonstrate the effectiveness of the STEF agent compared with competitive baselines.
Background:
The overall suicide rate in China has dropped substantially since the 1990s, but a slowdown in the decrease and even a reversing trend was observed in specific groups in recent years. This study aims to investigate the latest suicide risk in mainland China by using the age-period-cohort (APC) analysis.
Method:
This population-based multiyear cross-sectional study included Chinese ages 10 to 84 years using data from the China Health Statistical Yearbook (2005-2020). Data were analyzed by the APC analysis and intrinsic estimator (IE) technique.
Results:
The data satisfactorily fit the constructed APC models. The cohort effect indicated a high risk of suicide among people birth in 1920-1944 and a sharp decline in the 1945-1979 cohort. The lowest risk occurred in the 1980-1994 cohort before a sharp increase in generation Z (birth years in 1995-2009). The period effect showed a declining trend since 2004. The age effect indicated that the suicide risk increased over time, except for a gradual decline from age 35 to 49. The suicide risk increased greatly in adolescents and reached the highest among the elderly.
Limitations:
The aggregated population-level data and the non-identifiability of the APC model could result in bias in the accuracy of results in this study.
Conclusions:
This study successfully updated the Chinese suicide risk from the age, period and cohort perspective using the latest available data (2004-2019). The findings enhance the understanding of suicide epidemiology and provide evidence supporting policies and strategies at the macro-level for suicide prevention and management. Immediate action is needed to focus on a national suicide prevention strategy that targets generation Z, adolescents and the elderly which will require a collaborative effort by government officials, public/community health planners and health care agencies.
