IDEAS home Printed from https://ideas.repec.org/a/eee/chsofr/v140y2020ics0960077920305208.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960077920305208
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.chaos.2020.110123?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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. 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).
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:chsofr:v:140:y:2020:i:c:s0960077920305208. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Thayer, Thomas R. (email available below). General contact details of provider: https://www.journals.elsevier.com/chaos-solitons-and-fractals .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.