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| Item Type: | Article |
|---|---|
| Title: | Social media big data-based research on the influencing factors of insomnia and spatiotemporal evolution |
| Creators Name: | Liu, Y., Luo, Q., Shen, H., Zhuang, S., Xu, C., Dong, Y., Sun, Y., Wang, S. and Deng, H. |
| Abstract: | 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. |
| Keywords: | Social Media, Insomnia, Geographically Weighted Regression Mode, Influencing Factors |
| Source: | IEEE Access |
| ISSN: | 2169-3536 |
| Publisher: | IEEE |
| Volume: | 8 |
| Page Range: | 41516-41529 |
| Date: | 28 February 2020 |
| Official Publication: | https://doi.org/10.1109/ACCESS.2020.2976881 |
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