IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v18y2021i12p6272-d572268.html
   My bibliography  Save this article

Analyzing Twitter Data to Evaluate People’s Attitudes towards Public Health Policies and Events in the Era of COVID-19

Author

Listed:
  • Meng Hsiu Tsai

    (Department of Computer Science and Engineering, University of Tennessee at Chattanooga, Chattanooga, TN 37403, USA)

  • Yingfeng Wang

    (Department of Computer Science and Engineering, University of Tennessee at Chattanooga, Chattanooga, TN 37403, USA)

Abstract

Policymakers and relevant public health authorities can analyze people’s attitudes towards public health policies and events using sentiment analysis. Sentiment analysis focuses on classifying and analyzing text sentiments. A Twitter sentiment analysis has the potential to monitor people’s attitudes towards public health policies and events. Here, we explore the feasibility of using Twitter data to build a surveillance system for monitoring people’s attitudes towards public health policies and events since the beginning of the COVID-19 pandemic. In this study, we conducted a sentiment analysis of Twitter data. We analyzed the relationship between the sentiment changes in COVID-19-related tweets and public health policies and events. Furthermore, to improve the performance of the early trained model, we developed a data preprocessing approach by using the pre-trained model and early Twitter data, which were available at the beginning of the pandemic. Our study identified a strong correlation between the sentiment changes in COVID-19-related Twitter data and public health policies and events. Additionally, the experimental results suggested that the data preprocessing approach improved the performance of the early trained model. This study verified the feasibility of developing a fast and low-human-effort surveillance system for monitoring people’s attitudes towards public health policies and events during a pandemic by analyzing Twitter data. Based on the pre-trained model and early Twitter data, we can quickly build a model for the surveillance system.

Suggested Citation

  • Meng Hsiu Tsai & Yingfeng Wang, 2021. "Analyzing Twitter Data to Evaluate People’s Attitudes towards Public Health Policies and Events in the Era of COVID-19," IJERPH, MDPI, vol. 18(12), pages 1-14, June.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:12:p:6272-:d:572268
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/18/12/6272/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/18/12/6272/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Brownson, R.C. & Chriqui, J.F. & Stamatakis, K.A., 2009. "Understanding evidence-based public health policy," American Journal of Public Health, American Public Health Association, vol. 99(9), pages 1576-1583.
    2. Sinnenberg, L. & Buttenheim, A.M. & Padrez, K. & Mancheno, C. & Ungar, L. & Merchant, R.M., 2017. "Twitter as a tool for health research: A systematic review," American Journal of Public Health, American Public Health Association, vol. 107(1), pages 1-8.
    3. Cynthia Chew & Gunther Eysenbach, 2010. "Pandemics in the Age of Twitter: Content Analysis of Tweets during the 2009 H1N1 Outbreak," PLOS ONE, Public Library of Science, vol. 5(11), pages 1-13, November.
    4. repec:aph:ajpbhl:10.2105/ajph.2016.303512_4 is not listed on IDEAS
    5. Carter, S.M. & Rychetnik, L. & Dietetics, P. & Lloyd, B. & Kerridge, I.H. & Baur, L. & Bauman, A. & Hooker, C. & Zask, A., 2011. "Evidence, Ethics, and values: A Framework for Health promotion," American Journal of Public Health, American Public Health Association, vol. 101(3), pages 465-472.
    Full references (including those not matched with items on IDEAS)

    Citations

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


    Cited by:

    1. Bharati Sanjay Ainapure & Reshma Nitin Pise & Prathiba Reddy & Bhargav Appasani & Avireni Srinivasulu & Mohammad S. Khan & Nicu Bizon, 2023. "Sentiment Analysis of COVID-19 Tweets Using Deep Learning and Lexicon-Based Approaches," Sustainability, MDPI, vol. 15(3), pages 1-21, January.
    2. Zhihang Liu & Jinlin Wu & Connor Y. H. Wu & Xinming Xia, 2024. "Shifting sentiments: analyzing public reaction to COVID-19 containment policies in Wuhan and Shanghai through Weibo data," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-13, December.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Luis-Millán González & José Devís-Devís & Maite Pellicer-Chenoll & Miquel Pans & Alberto Pardo-Ibañez & Xavier García-Massó & Fernanda Peset & Fernanda Garzón-Farinós & Víctor Pérez-Samaniego, 2021. "The Impact of COVID-19 on Sport in Twitter: A Quantitative and Qualitative Content Analysis," IJERPH, MDPI, vol. 18(9), pages 1-20, April.
    2. Amir Haghighati & Kamran Sedig, 2020. "VARTTA: A Visual Analytics System for Making Sense of Real-Time Twitter Data," Data, MDPI, vol. 5(1), pages 1-25, February.
    3. Nisar, Sobia & Shafiq, Muhammad, 2019. "Framework for efficient utilisation of social media in Pakistan's healthcare sector," Technology in Society, Elsevier, vol. 56(C), pages 31-43.
    4. Han, Chunjia & Yang, Mu & Piterou, Athena, 2021. "Do news media and citizens have the same agenda on COVID-19? an empirical comparison of twitter posts," Technological Forecasting and Social Change, Elsevier, vol. 169(C).
    5. Anni Arumsari Fitriany & Piotr J. Flatau & Khoirunurrofik Khoirunurrofik & Nelly Florida Riama, 2021. "Assessment on the Use of Meteorological and Social Media Information for Forest Fire Detection and Prediction in Riau, Indonesia," Sustainability, MDPI, vol. 13(20), pages 1-13, October.
    6. Oliver J. Canfell & Kamila Davidson & Clair Sullivan & Elizabeth E. Eakin & Andrew Burton-Jones, 2022. "PREVIDE: A Qualitative Study to Develop a Decision-Making Framework (PREVention decIDE) for Noncommunicable Disease Prevention in Healthcare Organisations," IJERPH, MDPI, vol. 19(22), pages 1-16, November.
    7. Guangyu Hu & Xueyan Han & Huixuan Zhou & Yuanli Liu, 2019. "Public Perception on Healthcare Services: Evidence from Social Media Platforms in China," IJERPH, MDPI, vol. 16(7), pages 1-10, April.
    8. Tae Ho Kim & Chang Sug Park & Sang-hyeok Lee & Jung Eun Kang, 2023. "Gap Analysis Between the Level of Heat Wave Adaptation Policy and Heat Wave Effects in South Korean Municipalities," Urban Planning, Cogitatio Press, vol. 8(2), pages 120-132.
    9. Rieckmann, Traci R. & Kovas, Anne E. & Cassidy, Elaine F. & McCarty, Dennis, 2011. "Employing policy and purchasing levers to increase the use of evidence-based practices in community-based substance abuse treatment settings: Reports from single state authorities," Evaluation and Program Planning, Elsevier, vol. 34(4), pages 366-374, November.
    10. Umar Ali Bukar & Fatimah Sidi & Marzanah A. Jabar & Rozi Nor Haizan Nor & Salfarina Abdullah & Iskandar Ishak & Mustafa Alabadla & Ali Alkhalifah, 2022. "How Advanced Technological Approaches Are Reshaping Sustainable Social Media Crisis Management and Communication: A Systematic Review," Sustainability, MDPI, vol. 14(10), pages 1-26, May.
    11. Boonyanit Mathayomchan & Viriya Taecharungroj & Walanchalee Wattanacharoensil, 2023. "Evolution of COVID-19 tweets about Southeast Asian Countries: topic modelling and sentiment analyses," Place Branding and Public Diplomacy, Palgrave Macmillan, vol. 19(3), pages 317-334, September.
    12. Elanor Colleoni & Nuccio Ludovico & Illia Laura & Ravindran Kiron, 2021. "Does Sharing Economy Have a Moral Capital? Comparing Semantic Networks in Social Media and News Media," Journal of Management and Sustainability, Canadian Center of Science and Education, vol. 11(2), pages 1-1, December.
    13. Yang, You-hong & Gao, Ping & Zhou, Haimei, 2023. "Understanding the evolution of China's standardization policy system," Telecommunications Policy, Elsevier, vol. 47(2).
    14. Nanath, Krishnadas & Balasubramanian, Sreejith & Shukla, Vinaya & Islam, Nazrul & Kaitheri, Supriya, 2022. "Developing a mental health index using a machine learning approach: Assessing the impact of mobility and lockdown during the COVID-19 pandemic," Technological Forecasting and Social Change, Elsevier, vol. 178(C).
    15. Cindy Cheng & Joan Barceló & Allison Spencer Hartnett & Robert Kubinec & Luca Messerschmidt, 2020. "COVID-19 Government Response Event Dataset (CoronaNet v.1.0)," Nature Human Behaviour, Nature, vol. 4(7), pages 756-768, July.
    16. Bruce Forrester, 2020. "Authentic chatter," Computational and Mathematical Organization Theory, Springer, vol. 26(4), pages 382-411, December.
    17. Greyling, Talita & Rossouw, Stephanie & Adhikari, Tamanna, 2020. "Happiness-lost: Did Governments make the right decisions to combat Covid-19?," GLO Discussion Paper Series 556, Global Labor Organization (GLO).
    18. Alfred Rütten, 2012. "Evidence-based policy revisited: orientation towards the policy process and a public health policy science," International Journal of Public Health, Springer;Swiss School of Public Health (SSPH+), vol. 57(3), pages 455-457, June.
    19. Hongzhou Shen & Yue Ju & Zhijing Zhu, 2023. "Extracting Useful Emergency Information from Social Media: A Method Integrating Machine Learning and Rule-Based Classification," IJERPH, MDPI, vol. 20(3), pages 1-20, January.
    20. Gaspar, Rui & Yan, Zheng & Domingos, Samuel, 2019. "Extreme natural and man-made events and human adaptive responses mediated by information and communication technologies' use: A systematic literature review," Technological Forecasting and Social Change, Elsevier, vol. 145(C), pages 125-135.

    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:gam:jijerp:v:18:y:2021:i:12:p:6272-:d:572268. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.