IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v15y2018i2p250-d129908.html
   My bibliography  Save this article

Using Twitter to Better Understand the Spatiotemporal Patterns of Public Sentiment: A Case Study in Massachusetts, USA

Author

Listed:
  • Xiaodong Cao

    (Department of Environmental Health, Harvard T.H. Chan School of Public Health, 401 Park Drive, Boston, MA 02215, USA)

  • Piers MacNaughton

    (Department of Environmental Health, Harvard T.H. Chan School of Public Health, 401 Park Drive, Boston, MA 02215, USA)

  • Zhengyi Deng

    (Department of Environmental Health, Harvard T.H. Chan School of Public Health, 401 Park Drive, Boston, MA 02215, USA)

  • Jie Yin

    (Department of Environmental Health, Harvard T.H. Chan School of Public Health, 401 Park Drive, Boston, MA 02215, USA)

  • Xi Zhang

    (Department of Environmental Health, Harvard T.H. Chan School of Public Health, 401 Park Drive, Boston, MA 02215, USA
    School of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai 200240, China)

  • Joseph G. Allen

    (Department of Environmental Health, Harvard T.H. Chan School of Public Health, 401 Park Drive, Boston, MA 02215, USA)

Abstract

Twitter provides a rich database of spatiotemporal information about users who broadcast their real-time opinions, sentiment, and activities. In this paper, we sought to investigate the holistic influence of land use and time period on public sentiment. A total of 880,937 tweets posted by 26,060 active users were collected across Massachusetts (MA), USA, through 31 November 2012 to 3 June 2013. The IBM Watson Alchemy API (application program interface) was employed to quantify the sentiment scores conveyed by tweets on a large scale. Then we statistically analyzed the sentiment scores across different spaces and times. A multivariate linear mixed-effects model was used to quantify the fixed effects of land use and the time period on the variations in sentiment scores, considering the clustering effect of users. The results exposed clear spatiotemporal patterns of users’ sentiment. Higher sentiment scores were mainly observed in the commercial and public areas, during the noon/evening and on weekends. Our findings suggest that social media outputs can be used to better understand the spatial and temporal patterns of public happiness and well-being in cities and regions.

Suggested Citation

  • Xiaodong Cao & Piers MacNaughton & Zhengyi Deng & Jie Yin & Xi Zhang & Joseph G. Allen, 2018. "Using Twitter to Better Understand the Spatiotemporal Patterns of Public Sentiment: A Case Study in Massachusetts, USA," IJERPH, MDPI, vol. 15(2), pages 1-15, February.
    • Handle: RePEc:gam:jijerp:v:15:y:2018:i:2:p:250-:d:129908
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/15/2/250/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/15/2/250/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Mor Naaman & Hila Becker & Luis Gravano, 2011. "Hip and trendy: Characterizing emerging trends on Twitter," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 62(5), pages 902-918, May.
    2. Aiman Soliman & Kiumars Soltani & Junjun Yin & Anand Padmanabhan & Shaowen Wang, 2017. "Social sensing of urban land use based on analysis of Twitter users’ mobility patterns," PLOS ONE, Public Library of Science, vol. 12(7), pages 1-16, July.
    3. Morais, Paulo & Camanho, Ana S., 2011. "Evaluation of performance of European cities with the aim to promote quality of life improvements," Omega, Elsevier, vol. 39(4), pages 398-409, August.
    4. Mor Naaman & Hila Becker & Luis Gravano, 2011. "Hip and trendy: Characterizing emerging trends on Twitter," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 62(5), pages 902-918, May.
    5. Lansley, Guy & Longley, Paul, 2016. "Deriving age and gender from forenames for consumer analytics," Journal of Retailing and Consumer Services, Elsevier, vol. 30(C), pages 271-278.
    6. Hyekyung Woo & Youngtae Cho & Eunyoung Shim & Kihwang Lee & Gilyoung Song, 2015. "Public Trauma after the Sewol Ferry Disaster: The Role of Social Media in Understanding the Public Mood," IJERPH, MDPI, vol. 12(9), pages 1-10, September.
    7. Dolan, Paul & Peasgood, Tessa & White, Mathew, 2008. "Do we really know what makes us happy A review of the economic literature on the factors associated with subjective well-being," Journal of Economic Psychology, Elsevier, vol. 29(1), pages 94-122, February.
    8. Oswald, Andrew J. & Wu, Stephen, 2010. "Objective Confirmation of Subjective Measures of Human Well-being: Evidence from the USA," IZA Discussion Papers 4695, Institute of Labor Economics (IZA).
    9. Marco Palomino & Tim Taylor & Ayse Göker & John Isaacs & Sara Warber, 2016. "The Online Dissemination of Nature–Health Concepts: Lessons from Sentiment Analysis of Social Media Relating to “Nature-Deficit Disorder”," IJERPH, MDPI, vol. 13(1), pages 1-23, January.
    10. David A Broniatowski & Michael J Paul & Mark Dredze, 2013. "National and Local Influenza Surveillance through Twitter: An Analysis of the 2012-2013 Influenza Epidemic," PLOS ONE, Public Library of Science, vol. 8(12), pages 1-1, December.
    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. Carlos de las Heras-Pedrosa & Pablo Sánchez-Núñez & José Ignacio Peláez, 2020. "Sentiment Analysis and Emotion Understanding during the COVID-19 Pandemic in Spain and Its Impact on Digital Ecosystems," IJERPH, MDPI, vol. 17(15), pages 1-22, July.
    2. Xuehua Han & Juanle Wang & Min Zhang & Xiaojie Wang, 2020. "Using Social Media to Mine and Analyze Public Opinion Related to COVID-19 in China," IJERPH, MDPI, vol. 17(8), pages 1-22, April.

    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. Hajdu, Tamás & Hajdu, Gábor, 2011. "A hasznosság és a relatív jövedelem kapcsolatának vizsgálata magyar adatok segítségével [Examining the relation of utility and relative income using Hungarian data]," Közgazdasági Szemle (Economic Review - monthly of the Hungarian Academy of Sciences), Közgazdasági Szemle Alapítvány (Economic Review Foundation), vol. 0(1), pages 56-73.
    2. Tomas Hanell, 2022. "Unmet Aspirations and Urban Malaise," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 164(1), pages 83-103, November.
    3. Susanne Väth & Michael Kirk, 2014. "Do property rights and contract farming matter for rural development? Evidence from a large-scale investment in Ghana," MAGKS Papers on Economics 201416, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    4. Shuo Xu & Liyuan Hao & Xin An & Hongshen Pang & Ting Li, 2020. "Review on emerging research topics with key-route main path analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 122(1), pages 607-624, January.
    5. Jan-Emmanuel De Neve & George Ward & Femke De Keulenaer & Bert Van Landeghem & Georgios Kavetsos & Michael I. Norton, 2018. "The Asymmetric Experience of Positive and Negative Economic Growth: Global Evidence Using Subjective Well-Being Data," The Review of Economics and Statistics, MIT Press, vol. 100(2), pages 362-375, May.
    6. D.P. Doessel & Ruth F.G. Williams, 2012. "The New Welfare Measures," Working Papers 2012.07, School of Economics, La Trobe University.
    7. George Ward, 2015. "Is Happiness a Predictor of Election Results?," CEP Discussion Papers dp1343, Centre for Economic Performance, LSE.
    8. Blanchflower, David G. & Oswald, Andrew J., 2011. "Antidepressants and Age," IZA Discussion Papers 5785, Institute of Labor Economics (IZA).
    9. Hania Wu & Tony Tam, 2015. "Economic Development and Socioeconomic Inequality of Well-Being: A Cross-Sectional Time-Series Analysis of Urban China, 2003–2011," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 124(2), pages 401-425, November.
    10. Easterlin, Richard A. & Angelescu, Laura & Zweig, Jacqueline S., 2011. "The Impact of Modern Economic Growth on Urban–Rural Differences in Subjective Well-Being," World Development, Elsevier, vol. 39(12), pages 2187-2198.
    11. Sarracino, Francesco & O'Connor, Kelsey J. & Ono, Hiroshi, 2019. "Making economic growth and well-being compatible: evidence from Japan," MPRA Paper 93010, University Library of Munich, Germany.
    12. Köke, Sonja & Perino, Grischa, 2017. "For "better" or "worse": a direct approach to elicit preference rankings from life-satisfaction data," WiSo-HH Working Paper Series 43, University of Hamburg, Faculty of Business, Economics and Social Sciences, WISO Research Laboratory.
    13. Arie Sherman & Tal Shavit, 2018. "The Thrill of Creative Effort at Work: An Empirical Study on Work, Creative Effort and Well-Being," Journal of Happiness Studies, Springer, vol. 19(7), pages 2049-2069, October.
    14. Philip M. Pendergast & Tim Wadsworth & Charis E. Kubrin, 2019. "Suicide in Happy Places: Is There Really a Paradox?," Journal of Happiness Studies, Springer, vol. 20(1), pages 81-99, January.
    15. Huan-Kai Peng & Hao-Chih Lee & Jia-Yu Pan & Radu Marculescu, 2016. "Data-Driven Engineering of Social Dynamics: Pattern Matching and Profit Maximization," PLOS ONE, Public Library of Science, vol. 11(1), pages 1-21, January.
    16. Junji Kageyama & Kazuma Sato, 2021. "Explaining the U-shaped life satisfaction: dissatisfaction as a driver of behavior," Journal of Bioeconomics, Springer, vol. 23(2), pages 179-202, July.
    17. Ward, George, 2015. "Is happiness a predictor of election results?," LSE Research Online Documents on Economics 61698, London School of Economics and Political Science, LSE Library.
    18. Bahadır Dursun & Resul Cesur, 2016. "Transforming lives: the impact of compulsory schooling on hope and happiness," Journal of Population Economics, Springer;European Society for Population Economics, vol. 29(3), pages 911-956, July.
    19. Zia, Syeda Hamna & Muneer, Naima & Siddiqui, Amna & Huda, Rozmeen, 2022. "The Impact of Perceived Social Media Activities On Consumer-Based Brand Equity: A Perspective from Emerging Economy," MPRA Paper 112346, University Library of Munich, Germany.
    20. Jiayin Pei & Guang Yu & Xianyun Tian & Maureen Renee Donnelley, 2017. "A new method for early detection of mass concern about public health issues," Journal of Risk Research, Taylor & Francis Journals, vol. 20(4), pages 516-532, April.

    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:15:y:2018:i:2:p:250-:d:129908. 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.