An Analysis of Anxiety-Related Postings on Sina Weibo

Tian, Xianyun; He, Fang; Batterham, Philip J.; Wang, Zheng; Yu, Guang

doi:10.3390/ijerph14070775

Cited by 14 publications

(12 citation statements)

References 21 publications

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“…It is worth noting that identified users in this study were not clinically diagnosed. Some of them might also be high on neuroticism just like the users identified in an anxiety-related study [ 40 ]. People who score high tend to express more feelings of anxiety or depression than average [ 41 , 42 ].…”

Section: Discussionmentioning

confidence: 99%

Characterizing Depression Issues on Sina Weibo

Tian

Batterham

Song

et al. 2018

IJERPH

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The prevalence of depression has increased significantly over the past few years both in developed and developing countries. However, many people with symptoms of depression still remain untreated or undiagnosed. Social media may be a tool to help researchers and clinicians to identify and support individuals who experience depression. More than 394,000,000 postings were collected from China’s most popular social media website, Sina Weibo. 1000 randomly selected depression-related postings was coded and analyzed to learn the themes of these postings, and a text classifier was built to identify the postings indicating depression. The identified depressed users were compared with the general population on demographic characteristics, diurnal patterns, and patterns of emoticon usage. We found that disclosure of depression was the most popular theme; depression displayers were more engaged with social media compared to non-depression displayers, the depression postings showed geographical variations, depression displayers tended to be active during periods of leisure and sleep, and depression displayers used negative emoticons more frequently than non-depression displayers. This study offers a broad picture of depression references on China’s social media, which may be cost effectively developed to detect and help individuals who may suffer from depression disorders.

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Section: Discussionmentioning

confidence: 99%

Characterizing Depression Issues on Sina Weibo

Tian

Batterham

Song

et al. 2018

IJERPH

Self Cite

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“…Golder et al [10] pioneered in using real-time user-generated textual data from Twitter and analyzing the daily changes of positive affect and negative affect, which was the flagship study involving social media data for analyzing emotion-related outcomes. Several following studies have shown the feasibility of using social media data to investigate various health-related issues in the general population, including sleep complaints, depression, anxiety, suicide, and HIV [11][12][13][14]. However, similar studies using social media data are rare for the HIV risk population, men who have sex with men (MSM).…”

Section: Introductionmentioning

confidence: 99%

Associations Between Affective States and Sexual and Health Status Among Men Who Have Sex With Men in China: Exploratory Study Using Social Media Data

et al. 2020

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Background Affective states, including sentiment and emotion, are critical determinants of health. However, few studies among men who have sex with men (MSM) have examined sentiment and emotion specifically using real-time social media technologies. Moreover, the explorations on their associations with sexual and health status among MSM are limited. Objective This study aimed to understand and examine the associations of affective states with sexual behaviors and health status among MSM using public data from the Blued (Blued International Inc) app. Methods A total of 843,745 public postings of 377,610 MSM users located in Guangdong were saved from the Blued app by automatic screen capture. Positive affect, negative affect, sexual behaviors, and health status were measured using the Simplified Chinese Linguistic Inquiry and Word Count. Emotions, including joy, sadness, anger, fear, and disgust, were measured using the Weibo Basic Mood Lexicon. A positive sentiment score and a positive emotion score were also calculated. Univariate and multivariate linear regression models on the basis of a permutation test were used to assess the associations of affective states with sexual behaviors and health status. Results A total of 5871 active MSM users and their 477,374 postings were finally selected. Both positive affect and positive emotions (eg, joy) peaked between 7 AM and 9 AM. Negative affect and negative emotions (eg, sadness and disgust) peaked between 2 AM and 4 AM. During that time, 25.1% (97/387) of negative postings were related to health and 13.4% (52/387) of negative postings were related to seeking social support. A multivariate analysis showed that the MSM who were more likely to post sexual behaviors were more likely to express positive affect (beta=0.3107; P<.001) and positive emotions (joy: beta=0.027; P<.001), as well as negative emotions (sadness: beta=0.0443; P<.001 and disgust: beta=0.0256; P<.001). They also had a higher positive sentiment score (beta=0.2947; P<.001) and a higher positive emotion score (beta=0.1612; P<.001). The MSM who were more likely to post their health status were more likely to express negative affect (beta=0.8088; P<.001) and negative emotions, including sadness (beta=0.0705; P<.001), anger (beta=0.0058; P<.001), fear (beta=0.0052; P<.001), and disgust (beta=0.3065; P<.001), and less likely to express positive affect (beta=−0.0224; P=.02). In addition, they had a lower positive sentiment score (beta=−0.8306; P<.001) and a lower positive emotion score (beta=−0.3743; P<.001). Conclusions The MSM social media community mainly expressed their positive affect in the early morning and negative affect after midnight. Positive affective states were associated with being sexually active, whereas negative affective states were associated with health problems, mostly about mental health. Our finding suggests the potential to deliver different health-related intervention strategies (eg, psychological counseling and safe sex promotion) on a social media app according to the affective states of MSM in real time.

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“…The Lindeman, Merenda, and Gold (LMG) model is used to evaluate the relative importance of each factor, and a geographic detector is applied to calculate the interaction of the influencing factors in step (3). The geographically weighted regression (GWR) model is used for the regression analysis in step (4).…”

Section: B Methods and Modelsmentioning

confidence: 99%

“…When no consensus was reached regarding the label, the majority decision principle was adopted. According to previous studies, the support vector machine (SVM) has high accuracy in Weibo text classification [3], [19]. Thus, the SVM was employed to distinguish the target Weibo texts.…”

Section: Data Collection and Preprocessing 1) Dependent Variablementioning

confidence: 99%

“…incidence climbs to 38.2%, which means that nearly 400 million people suffer from insomnia; worse still, this percentage continues to increase every year. Furthermore, more than 60% of the people affected by insomnia were born after 1990 [3], [4]. Insomnia can easily lead to a variety of personal health problems, such as fatigue, endocrine disorders, decreased immunity and other physical and mental illnesses [5], [6].…”

Section: Introductionmentioning

confidence: 99%

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Social Media Big Data-Based Research on the Influencing Factors of Insomnia and Spatiotemporal Evolution

et al. 2020

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Insomnia is a prevalent sleep disorder that causes serious harm to individuals and society. It is closely linked to not only personal factors but also social, economic and other factors. This study explores the influencing factors and spatial differentiation of insomnia from the perspective of social media. This paper chose China's largest social media platform, Sina Weibo, as its data source. Then, based on the collected relevant data of 288 Chinese cities from 2013 to 2017, it explored the impact of economic, social, and environmental factors and an educated population on insomnia. Additionally, the importance and interaction of each influencing factor were analyzed. According to the results, the gross domestic product (GDP), proportion of households connected to the Internet and number of students in regular institutions of higher education are the major factors that influence insomnia, and their influences show obvious spatial nonstationarity. Rapid GDP growth has increased the probability of insomnia, and the positive correlation between the proportion of households connected to the internet and insomnia has strengthened annually. Although the impact of insomnia on college students decreased in some regions, the overall impact was still increasing annually, and spatial nonstationarity was obvious. Properly controlling GDP growth and unnecessary time spent online and guiding people to develop healthy Internet surfing habits and lifestyles will help improve their sleep quality. Our research results will help relevant professionals better understand the distribution of regional insomnia and provide a reference for related departments to formulate regional insomnia prevention and treatment policies.INDEX TERMS Social media, insomnia, geographically weighted regression model, influencing factors.

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An Analysis of Anxiety-Related Postings on Sina Weibo

Cited by 14 publications

References 21 publications

Characterizing Depression Issues on Sina Weibo

Characterizing Depression Issues on Sina Weibo

Associations Between Affective States and Sexual and Health Status Among Men Who Have Sex With Men in China: Exploratory Study Using Social Media Data

Social Media Big Data-Based Research on the Influencing Factors of Insomnia and Spatiotemporal Evolution

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