Developmental Trend of Subjective Well-Being of Weibo Users During COVID-19: Online Text Analysis Based on Machine Learning Method

Parental dysfunction and adolescent mental health: AI-aided content analysis of suicide notes on social media

Adolescent suicide represents a critical global health issue. While research has identified numerous risk factors, the specific impact of parental dysfunction on adolescent suicide remains understudied, especially in Chinese contexts. This study explores how parental dysfunction manifests in suicide notes and affects adolescent mental health. We collected data from Chinese social media platforms using web crawlers, yielding 30 valid suicide notes for analysis. Using the AI-aided content analysis platform DiVoMiner®, we conducted high-frequency word and semantic network analyses. Our findings reveal that parents are a central concern for suicidal youth. We identified three primary patterns of parental dysfunction: excessive emphasis on instrumental goals, neglect of basic emotional needs, and inadequate protection from life traumas. These dysfunctions contribute to severe psychological distress, identity loss, and negative coping behaviors among youth. The research highlights two significant phenomena in contemporary Chinese family dynamics: the "short-sightedness" of prioritizing short-term instrumental goals over long-term social-emotional development, and the remarkably high prevalence of "lack of autonomy" in parenting approaches. Our study extends the literature by exploring mechanisms through which parental dysfunctions contribute to suicidal behaviors in young people. These findings emphasize the need for collaborative efforts among parents, educators, policymakers, and mental health professionals to foster nurturing environments characterized by emotional support, autonomy encouragement, and balanced academic expectations-all crucial for adolescent well-being.

Depression in the COVID-19 endemic era: Analysis of online self-disclosures by young South Koreans

Although COVID-19 has been declared endemic in South Korea, there are economic and psychosocial after-effects. One of these is the prevalence of depression. Depressed adolescents and young adults struggle with insecurity, loneliness, and lack of confidence due to the life limitations imposed during the pandemic. Young South Koreans experienced deterioration in mental health because of the recurrence of mass infections. To address professionals' concerns about the lingering effects of COVID-19 on youth mental health, we text-mined young South Koreans' online posts about depression during the pandemic and the endemic phases-from February 2020 to May 2023. We used a total of 1,740 selected posts (raw data publicly available on https://github.com/kimalexis1129/PLOS_endemic_depression.git) to explore the situational triggers, additional factors, and by-products of depression that have persisted during the endemic era. We used Latent Dirichlet allocation and Dirichlet-multinomial regression topic modeling methods in conjunction with sentiment analysis and mean comparison. The results showed that the pandemic and endemic topic models shared similarities, but emerging topics showed extended adversities such as adolescents' vulnerability to eating disorders and young adults' tendency to self-isolate. Comparisons between the levels of positive and negative affect during the pandemic and endemic eras revealed no significant changes in mood. We discussed the results in comparison with SARS and MERS precedents and from general and cultural perspectives.

Attention and sentiment of Chinese public toward rural landscape based on Sina Weibo

Rural landscapes, as products of the interaction between humans and nature, not only reflect the history and culture of rural areas but also symbolize economic and social progress. This study proposes a deep learning-based model for Weibo data analysis aimed at exploring the development direction of rural landscapes from the perspective of the Chinese public. The research reveals that the Chinese public's attention to rural landscapes has significantly increased with the evolution of government governance concepts. Most people express a high level of satisfaction and happiness with the existing rural landscapes, while a minority harbor negative emotions towards unreasonable new rural construction. Through the analysis of public opinion regarding rural landscapes, this study will assist decision-makers in understanding the mechanisms of public discourse on social media. It will also aid relevant scholars and designers in providing targeted solutions, which hold significant importance for policy formulation and the exploration of specific development patterns.

Analysis of the evolving factors of social media users' emotions and behaviors: a longitudinal study from China's COVID-19 opening policy period

The outbreak of the COVID-19 pandemic has triggered citizen panic and social crises worldwide. The Chinese government was the first to implement strict prevention and control policies. However, in December 2022, the Chinese government suddenly changed its prevention and control policies and completely opened up. This led to a large-scale infection of the epidemic in a short period of time, which will cause unknown social impacts. This study collected 500+ epidemic-related hotspots and 200,000+ data from November 1, 2022, to March 1, 2023. Using a sentiment classification method based on pre-trained neural network models, we conducted inductive analysis and a summary of high-frequency words of various emotions. This study focuses on the inflection point of the emotional evolution of social media users and the evolution of "hot topic searches" events and emotional behavioral factors after the sudden open policy. Our research results show that, first of all, the positive emotions of social media users are divided into 4 inflection points and 5 time periods, and the negative emotions are divided into 3 inflection points and 4 time periods. Behavioral factors are different at each stage of each emotion. And the evolution patterns of positive emotions and negative emotions are also different. Secondly, the evolution of behavioral elements deserves more attention. Continue to pay attention: The treatment of diseases, the recovery of personal health, the promotion of festive atmosphere, and the reduction of publicity on the harm of "new crown sequelae and second infections" are the behavioral concerns that affect users' emotional changes. Finally, it is necessary to change the "hot topic searches" event by guiding the user's behavioral focus to control the inflection point of the user's emotion. This study helps governments and institutions understand the dynamic impact of epidemic policy changes on social media users, thereby promoting policy formulation and better coping with social crises.

The positive energy of netizens: development and application of fine-grained sentiment lexicon and emotional intensity model

The outbreak of COVID-19 has led to a global health crisis and caused huge emotional swings. However, the positive emotional expressions, like self-confidence, optimism, and praise, that appear in Chinese social networks are rarely explored by researchers. This study aims to analyze the characteristics of netizens' positive energy expressions and the impact of node events on public emotional expression during the COVID-19 pandemic. First, a total of 6,525,249 Chinese texts posted by Sina Weibo users were randomly selected through textual data cleaning and word segmentation for corpus construction. A fine-grained sentiment lexicon that contained POSITIVE ENERGY was built using Word2Vec technology; this lexicon was later used to conduct sentiment category analysis on original posts. Next, through manual labeling and multi-classification machine learning model construction, four mainstream machine learning algorithms were selected to train the emotional intensity model. Finally, the lexicon and optimized emotional intensity model were used to analyze the emotional expressions of Chinese netizens. The results show that POSITIVE ENERGY expression accounted for 40.97% during the COVID-19 pandemic. Over the course of time, POSITIVE ENERGY emotions were displayed at the highest levels and SURPRISES the lowest. The analysis results of the node events showed after the outbreak was confirmed officially, the expressions of POSITIVE ENERGY and FEAR increased simultaneously. After the initial victory in pandemic prevention and control, the expression of POSITIVE ENERGY and SAD reached a peak, while the increase of SAD was the most prominent. The fine-grained sentiment lexicon, which includes a POSITIVE ENERGY category, demonstrated reliable algorithm performance and can be used for sentiment classification of Chinese Internet context. We also found many POSITIVE ENERGY expressions in Chinese online social platforms which are proven to be significantly affected by nod events of different nature.