IDEAS home Printed from https://ideas.repec.org/a/spr/jcsosc/v7y2024i3d10.1007_s42001-024-00311-5.html
   My bibliography  Save this article

Comparing location-specific and location-open social media data: methodological lessons from a study of blaming of minorities on Twitter during the COVID-19 pandemic

Author

Listed:
  • Shiyi Zhang

    (University of Leicester)

  • Panayiota Tsatsou

    (Birmingham City University)

  • Lauren McLaren

    (University of Leicester)

  • Yimei Zhu

    (University of Leicester)

Abstract

Social media platforms such as Twitter (currently X) have become important sites of public discourse and participation. Researchers have attempted to identify and collect Twitter data within a certain country or region in order to answer research questions within a particular locale. However, location information of tweets is limited. Tackling the case of public blaming of minorities on Twitter in the context of the COVID-19 pandemic in the UK, we present a method for identifying UK-based tweets and analyse two types of datasets that we collected and processed: (a) tweets with UK location-tags (labelled as location-specific data and referred to as UK datasets); and (b) tweets with UK location-tags and / or user profiles containing potential UK location information (labelled as location-open data and referred to as ALL datasets). The empirical results reveal that the overall sentiments in the two dataset types align in the same direction, but the location-specific datasets contain more extreme discourses (i.e., more positive and more negative sentiments and fewer neutral sentiments). Furthermore, in the location-specific datasets, the range of theme areas is narrower, although the themes still grasp the essence of the discussion about blaming minorities found in the larger dataset. The findings demonstrate strengths and limitations of the two dataset types and that the location-specific data can be suitable especially when the available research resources are insufficient for collecting or processing larger datasets. Nevertheless, we propose that future research may consider comparing smaller and bigger datasets to test differences between these for other topics for which specific locations may be of particular interest.

Suggested Citation

  • Shiyi Zhang & Panayiota Tsatsou & Lauren McLaren & Yimei Zhu, 2024. "Comparing location-specific and location-open social media data: methodological lessons from a study of blaming of minorities on Twitter during the COVID-19 pandemic," Journal of Computational Social Science, Springer, vol. 7(3), pages 2457-2479, December.
    • Handle: RePEc:spr:jcsosc:v:7:y:2024:i:3:d:10.1007_s42001-024-00311-5
    DOI: 10.1007/s42001-024-00311-5
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s42001-024-00311-5
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s42001-024-00311-5?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.

    References listed on IDEAS

    as
    1. Delgosha, Mohammad Soltani & Hajiheydari, Nastaran & Talafidaryani, Mojtaba, 2022. "Discovering IoT implications in business and management: A computational thematic analysis," Technovation, Elsevier, vol. 118(C).
    2. Dionne, Kim Yi & Turkmen, Fulya Felicity, 2020. "The Politics of Pandemic Othering: Putting COVID-19 in Global and Historical Context," International Organization, Cambridge University Press, vol. 74(S1), pages 213-230, December.
    3. Neslihan Bisgin & Halil Bisgin & Daniel Hummel & Jon Zelner & Belinda L. Needham, 2023. "Did the public attribute the Flint Water Crisis to racism as it was happening? Text analysis of Twitter data to examine causal attributions to racism during a public health crisis," Journal of Computational Social Science, Springer, vol. 6(1), pages 165-190, April.
    4. Rudy Arthur & Hywel T P Williams, 2019. "Scaling laws in geo-located Twitter data," PLOS ONE, Public Library of Science, vol. 14(7), pages 1-20, July.
    5. Eric Klinenberg, 2005. "Convergence: News Production in a Digital Age," The ANNALS of the American Academy of Political and Social Science, , vol. 597(1), pages 48-64, January.
    Full references (including those not matched with items on IDEAS)

    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. Aleem Mahabir & Romario Anderson & Robert Kinlocke & Rose-Ann Smith & Kristinia Doughorty & Chandradath Madho, 2022. "Discourse, Difference, and Divergence: Exploring Media Representations and Online Public Sentiments toward Marginalized Urban Communities in Jamaica during the COVID-19 Pandemic," Social Sciences, MDPI, vol. 11(6), pages 1-18, May.
    2. Yoo, Nari & Jang, Sou Hyun, 2024. "Does social empathy moderate fear-induced minority blaming during the COVID-19 pandemic?," Social Science & Medicine, Elsevier, vol. 346(C).
    3. Abdullahi Abdi Sheikh, 2020. "Will Newsrooms Continue to Be Relevant? Reflections on the Practices of Journalism in Kenya during Covid-19 Crisis," International Journal of Research and Scientific Innovation, International Journal of Research and Scientific Innovation (IJRSI), vol. 7(6), pages 01-05, June.
    4. Dušan Ristić & Dušan Marinković, 2022. "Biopolitics of othering during the COVID-19 pandemic," Palgrave Communications, Palgrave Macmillan, vol. 9(1), pages 1-8, December.
    5. Carol Nash, 2021. "Challenges to Learners in Interpreting Self as Other, Post COVID-19," Challenges, MDPI, vol. 12(2), pages 1-24, November.
    6. Ho, Kung-Cheng & Yao, Chia-ling & Zhao, Chenfang & Pan, Zikui, 2022. "Modern health pandemic crises and stock price crash risk," Economic Analysis and Policy, Elsevier, vol. 74(C), pages 448-463.
    7. Kumar, Mukesh & Raut, Rakesh D. & Mangla, Sachin Kumar & Chowdhury, Soumyadeb & Choubey, Vikas Kumar, 2024. "Moderating ESG compliance between industry 4.0 and green practices with green servitization: Examining its impact on green supply chain performance," Technovation, Elsevier, vol. 129(C).
    8. Clarysse, Bart & He, Vivianna Fang & Tucci, Christopher L., 2022. "How the Internet of Things reshapes the organization of innovation and entrepreneurship," Technovation, Elsevier, vol. 118(C).
    9. Lonnie R. Snowden & Jonathan M. Snowden, 2021. "Coronavirus Trauma and African Americans’ Mental Health: Seizing Opportunities for Transformational Change," IJERPH, MDPI, vol. 18(7), pages 1-10, March.
    10. Bo Zhou & Lei Jiang, 2022. "Unsustainable Urban Development Based on Temporary Workers: A Study on the Changes of Immigration in Macau between 1992 and 2019," Land, MDPI, vol. 11(11), pages 1-19, November.
    11. Jang, Hyejin & Lee, Suyeong & Yoon, Byungun, 2023. "Data-driven techno-socio co-evolution analysis based on a topic model and a hidden Markov model," Technovation, Elsevier, vol. 126(C).
    12. Youngwan Kim & Sang-Hwan Lee & Young Jun Cho, 2023. "Donor motivation in the era of the COVID-19 crisis: Focusing on South Korean health diplomacy and response aid to COVID-19," International Area Studies Review, Center for International Area Studies, Hankuk University of Foreign Studies, vol. 26(1), pages 71-86, March.
    13. Pramod R. Regmi & Shovita Dhakal Adhikari & Nirmal Aryal & Sharada P. Wasti & Edwin van Teijlingen, 2022. "Fear, Stigma and Othering: The Impact of COVID-19 Rumours on Returnee Migrants and Muslim Populations of Nepal," IJERPH, MDPI, vol. 19(15), pages 1-14, July.
    14. Ojong, Nathanael & Agbe, Eyram, 2023. "“This is most likely not the correct vaccine”: Analyzing COVID-19's viral spread and vaccine anxieties in Ghana, Cameroon, and Malawi," Social Science & Medicine, Elsevier, vol. 329(C).

    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:spr:jcsosc:v:7:y:2024:i:3:d:10.1007_s42001-024-00311-5. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.