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Analysis of spatiotemporal characteristics of big data on social media sentiment with COVID-19 epidemic topics

Author

Listed:
  • Zhu, Bangren
  • Zheng, Xinqi
  • Liu, Haiyan
  • Li, Jiayang
  • Wang, Peipei

Abstract

COVID-19 blocked Wuhan in China, which was sealed off on Chinese New Year's Eve. During this period, the research on the relevant topics of COVID-19 and emotional expressions published on social media can provide decision support for the management and control of large-scale public health events. The research assisted the analysis of microblog text topics with the help of the LDA model, and obtained 8 topics (“origin”, “host”, “organization”, “quarantine measures”, “role models”, “education”, “economic”, “rumor”) and 28 interactive topics. Obtain data through crawler tools, with the help of big data technology, social media topics and emotional change characteristics are analyzed from spatiotemporal perspectives. The results show that: (1) “Double peaks” feature appears in the epidemic topic search curve. Weibo on the topic of the epidemic gradually reduced after January 24. However, the proportion of epidemic topic searches has gradually increased, and a “double peaks” phenomenon appeared within a week; (2) The topic changes with time and the fluctuation of the topic discussion rate gradually weakens. The number of texts on different topics and interactive topics changes with time. At the same time, the discussion rate of epidemic topics gradually weakens; (3) The political and economic center is an area where social media is highly concerned. The areas formed by Beijing, Shanghai, Guangdong, Sichuan and Hubei have published more microblog texts. The spatial division of the number of Weibo social media texts has a high correlation with the economic zone division; (4) The existence of the topic of “rumor” will enable people to have more communication and discussion. The interactive topics of “rumors” always have higher topic popularity and low emotion text expressions. Through the analysis of media information, it helps relevant decision makers to grasp social media topics from spatiotemporal characteristics, so that relevant departments can accurately grasp the public's subjective ideas and emotional expressions, and provide decision support for macro-control response strategies and measures and risk communication.

Suggested Citation

  • Zhu, Bangren & Zheng, Xinqi & Liu, Haiyan & Li, Jiayang & Wang, Peipei, 2020. "Analysis of spatiotemporal characteristics of big data on social media sentiment with COVID-19 epidemic topics," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    • Handle: RePEc:eee:chsofr:v:140:y:2020:i:c:s0960077920305208
    DOI: 10.1016/j.chaos.2020.110123
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    Citations

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    Cited by:

    1. Qiang Wang & Min Su & Min Zhang & Rongrong Li, 2021. "Integrating Digital Technologies and Public Health to Fight Covid-19 Pandemic: Key Technologies, Applications, Challenges and Outlook of Digital Healthcare," IJERPH, MDPI, vol. 18(11), pages 1-50, June.
    2. Yongqiang Zhao & Liwei Zhang, 2022. "An Advanced Study of Urban Emergency Medical Equipment Logistics Distribution for Different Levels of Urgency Demand," IJERPH, MDPI, vol. 19(18), pages 1-16, September.
    3. Lidia Perenc & Justyna Podgórska-Bednarz & Agnieszka Guzik & Mariusz Drużbicki, 2023. "Impact of the COVID-19 Pandemic on the Level of Anxiety and Depression in Caregivers of Children Benefiting from Neurorehabilitation Services," IJERPH, MDPI, vol. 20(5), pages 1-19, March.
    4. Zijing Ye & Ruisi Li & Jing Wu, 2022. "Dynamic Demand Evaluation of COVID-19 Medical Facilities in Wuhan Based on Public Sentiment," IJERPH, MDPI, vol. 19(12), pages 1-22, June.
    5. Israel Edem Agbehadji & Bankole Osita Awuzie & Alfred Beati Ngowi, 2021. "COVID-19 Pandemic Waves: 4IR Technology Utilisation in Multi-Sector Economy," Sustainability, MDPI, vol. 13(18), pages 1-20, September.
    6. Adnan Muhammad Shah & Rizwan Ali Naqvi & Ok-Ran Jeong, 2021. "Detecting Topic and Sentiment Trends in Physician Rating Websites: Analysis of Online Reviews Using 3-Wave Datasets," IJERPH, MDPI, vol. 18(9), pages 1-25, April.
    7. Iustina Alina Boitan & Emilia Mioara Campeanu & Sanja Sever Malis, 2021. "Economic Sentiment Perceptions During COVID-19 Pandemic – A European Cross-Country Impact Assessment," The AMFITEATRU ECONOMIC journal, Academy of Economic Studies - Bucharest, Romania, vol. 23(Special15), pages 982-982, November.
    8. Hainan Huang & Weifan Chen & Tian Xie & Yaoyao Wei & Ziqing Feng & Weijiong Wu, 2021. "The Impact of Individual Behaviors and Governmental Guidance Measures on Pandemic-Triggered Public Sentiment Based on System Dynamics and Cross-Validation," IJERPH, MDPI, vol. 18(8), pages 1-25, April.
    9. Liu, Jia & Bai, Jinyu & Wu, Desheng, 2021. "Medical supplies scheduling in major public health emergencies," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 154(C).
    10. Luanying Li & Lin Hua & Fei Gao, 2022. "What We Ask about When We Ask about Quarantine? Content and Sentiment Analysis on Online Help-Seeking Posts during COVID-19 on a Q&A Platform in China," IJERPH, MDPI, vol. 20(1), pages 1-19, December.
    11. Wen-zhong Shi & Fanxin Zeng & Anshu Zhang & Chengzhuo Tong & Xiaoqi Shen & Zhewei Liu & Zhicheng Shi, 2022. "Online public opinion during the first epidemic wave of COVID-19 in China based on Weibo data," Palgrave Communications, Palgrave Macmillan, vol. 9(1), pages 1-10, December.

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