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

Han, Yingying; Pan, Wenhao; Li, Jinjin; Zhang, Ting; Zhang, Qiang; Zhang, Emily

doi:10.3389/fpsyg.2021.779594

Cited by 5 publications

(6 citation statements)

References 57 publications

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“…Web-based social media platforms can serve as e cient tools for tracking people's reactions to a series of news regarding a public event in a timely manner [ 8] .Thanks to the wide usage of social media, the online text database has become a "powerful tool" for the real-time monitoring of changes in an emotional state, SWB, and other psychological traits of its users. Sina Weibo is one of the major Chinese online social media in the Web 3.0 era and provides users with a platform for building social networks [ 9] .…”

Section: Introductionmentioning

confidence: 99%

Understand Public Perspectives about “Industrial worker health” Based on Weibo Data : Content Analysis and Topic Modeling Study

Wang,

Yao,

et al. 2024

Preprint

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Background Promoting the construction of a healthy China is a national strategy to comprehensively enhance the health quality of the Chinese nation and achieve coordinated development between people’s health and the economy and society. Strengthening safety production and occupational health work, improving working conditions, and enhancing the health quality of industrial workers are important aspects of building a healthy China. However, there is currently a lack of research on the content of speeches on online platforms such as Sina Weibo. Objective This study used a web crawler approach to obtain people’s opinions about “Industrial worker health”.We investigated the value of Chinese social media and related public perceptions and response. The goal was to determine if this data is suitable for public response.Through this exploration, the paper seeks to provide evidence-based recommendations to enhance the alignment of digital public opinion with the overarching goals of the “Healthy China” strategy, ultimately promoting the health and welfare of the industrial workforce. Methods This paper employed cutting-edge web scraping techniques to harvest data from the prominent social media platform, Sina Weibo. Through the application of sophisticated data collection tools, specifically the “Houyi Collector”, the study focuses on the keyword “industrial workers' health” to extract relevant information from Sina Weibo, thereby capturing the pulse of online discussions regarding the health concerns and awareness of industrial laborers.The gathered dataset is then meticulously analyzed using the Latent Dirichlet Allocation (LDA) topic modeling technique to identify and categorize the principal themes and trends that prevail in the online conversations. This analytic approach is intended to uncover the breadth and trajectory of public attention and attitudes toward the well-being of industrial workers within the context of the “Healthy China” initiative. Results By illuminating the digital footprint of health-related industrial worker discourses on a major Chinese social media platform, this research contributes a nuanced understanding of the virtual labor community’s engagement with health issues. Conclusions The insights derived from the LDA topic modeling are set to inform stakeholders, including policymakers, health advocates, and industrial enterprises, regarding the focal points and sentiments that shape the online narrative on industrial workers’ health in China.

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Section: Introductionmentioning

confidence: 99%

Understand Public Perspectives about “Industrial worker health” Based on Weibo Data : Content Analysis and Topic Modeling Study

Wang,

Yao,

et al. 2024

Preprint

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“…The psychology of ordinary people has also changed after the long-term development of the epidemic [ 21 ]. This kind of longitudinal research that emphasizes “temporal dynamic trends” is rare in previous studies [ 22 ]. Because of the discovery of these complex issues, it is currently unclear how the impact of epidemic policies will evolve over time, which may be related to specific influencing factors.…”

Section: Introductionmentioning

confidence: 99%

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

Zhang,

Yang,

Niu

et al. 2023

BMC Public Health

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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.

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“…As one of the most popular social media platforms in China, Sina Weibo (Weibo) has aggregated huge numbers of sadness, fear, anger or happy texts about some hot issues (Sina Weibo Data Center, 2021 ; Zhang & Yu, 2018 ). Mining and exploring this kind of massive data from social media is of great significance to the correct and objective understanding of the emotions and attitudes of netizens, and to studying the emotional expression of individuals in the network environment (Han et al, 2022 ). Meanwhile, it can also provide theoretical reference for government departments to recognize and guide trends related to public opinion evolution.…”

Section: Introductionmentioning

confidence: 99%

“…Salathé and Khandelwal ( 2011 ) investigated the emotional expression of Twitter users during the H1N1 pandemic and found that the emotional intensity of Twitter users showed the characteristics of temporal and spatial changes with the development of the H1N1 pandemic (Salathé & Khandelwal, 2011 ). In general, while sentiment analysis has become a mainstream research method for studying individual emotional expression (Feng et al, 2020 ; Kirelli & Arslankaya, 2020 ), the use of NLP technology to analyze the emotional expression of users in public health emergencies has also been proven feasible (Han et al, 2022 ; You et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

“…The outbreak of the Coronavirus Disease 2019 (COVID-19) was first reported in Wuhan, China, subsequently, cases of 2019 novel coronavirus pneumonia have been reported worldwide (World Health Organization, 2020 ). The COVID-19 outbreak has generated significant panic and worry (Han et al, 2022 ; Nicomedes & Avila, 2020 ). In some ways, the COVID-19 pandemic broadly holds the world in its grip (Blanken et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

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

Pan

Han

et al. 2022

Curr Psychol

Self Cite

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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.

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

Cited by 5 publications

References 57 publications

Understand Public Perspectives about “Industrial worker health” Based on Weibo Data : Content Analysis and Topic Modeling Study

Understand Public Perspectives about “Industrial worker health” Based on Weibo Data : Content Analysis and Topic Modeling Study

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

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

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