IDEAS home Printed from https://ideas.repec.org/a/gam/jdataj/v4y2019i2p84-d238501.html
   My bibliography  Save this article

Pedagogical Demonstration of Twitter Data Analysis: A Case Study of World AIDS Day, 2014

Author

Listed:
  • Isaac Chun-Hai Fung

    (Department of Biostatistics, Epidemiology and Environmental Health Sciences, Jiann-Ping Hsu College of Public Health, Georgia Southern University, Statesboro, GA 30460, USA)

  • Jingjing Yin

    (Department of Biostatistics, Epidemiology and Environmental Health Sciences, Jiann-Ping Hsu College of Public Health, Georgia Southern University, Statesboro, GA 30460, USA)

  • Keisha D. Pressley

    (Department of Biostatistics, Epidemiology and Environmental Health Sciences, Jiann-Ping Hsu College of Public Health, Georgia Southern University, Statesboro, GA 30460, USA)

  • Carmen H. Duke

    (Department of Biostatistics, Epidemiology and Environmental Health Sciences, Jiann-Ping Hsu College of Public Health, Georgia Southern University, Statesboro, GA 30460, USA)

  • Chen Mo

    (Department of Biostatistics, Epidemiology and Environmental Health Sciences, Jiann-Ping Hsu College of Public Health, Georgia Southern University, Statesboro, GA 30460, USA)

  • Hai Liang

    (School of Journalism and Communication, Chinese University of Hong Kong, Hong Kong Special Administrative Region, China)

  • King-Wa Fu

    (Journalism and Media Studies Centre, The University of Hong Kong, HongKong, China)

  • Zion Tsz Ho Tse

    (School of Electrical and Computer Engineering, College of Engineering, The University of Georgia, Athens, GA 30602, USA)

  • Su-I Hou

    (College of Community Innovation and Education, The University of Central Florida, Orlando, FL 32816, USA)

Abstract

As a pedagogical demonstration of Twitter data analysis, a case study of HIV/AIDS-related tweets around World AIDS Day, 2014, was presented. This study examined if Twitter users from countries with various income levels responded differently to World AIDS Day. The performance of support vector machine (SVM) models as classifiers of relevant tweets was evaluated. A manual coding of 1,826 randomly sampled HIV/AIDS-related original tweets from November 30 through December 2, 2014 was completed. Logistic regression was applied to analyze the association between the World Bank-designated income level of users’ self-reported countries and Twitter contents. To identify the optimal SVM model, 1278 (70%) of the 1826 sampled tweets were randomly selected as the training set, and 548 (30%) served as the test set. Another 180 tweets were separately sampled and coded as the held-out dataset. Compared with tweets from low-income countries, tweets from the Organization for Economic Cooperation and Development countries had 60% lower odds to mention epidemiology (adjusted odds ratio, aOR = 0.404; 95% CI: 0.166, 0.981) and three times the odds to mention compassion/support (aOR = 3.080; 95% CI: 1.179, 8.047). Tweets from lower-middle-income countries had 79% lower odds than tweets from low-income countries to mention HIV-affected sub-populations (aOR = 0.213; 95% CI: 0.068, 0.664). The optimal SVM model was able to identify relevant tweets from the held-out dataset of 180 tweets with an accuracy (F1 score) of 0.72. This study demonstrated how students can be taught to analyze Twitter data using manual coding, regression models, and SVM models.

Suggested Citation

  • Isaac Chun-Hai Fung & Jingjing Yin & Keisha D. Pressley & Carmen H. Duke & Chen Mo & Hai Liang & King-Wa Fu & Zion Tsz Ho Tse & Su-I Hou, 2019. "Pedagogical Demonstration of Twitter Data Analysis: A Case Study of World AIDS Day, 2014," Data, MDPI, vol. 4(2), pages 1-12, June.
  • Handle: RePEc:gam:jdataj:v:4:y:2019:i:2:p:84-:d:238501
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2306-5729/4/2/84/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2306-5729/4/2/84/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Young, S.D. & Holloway, I. & Jaganath, D. & Rice, E. & Westmoreland, D. & Coates, T., 2014. "Project HOPE: Online social network changes in an HIV prevention randomized controlled trial for African American and Latino men who have sex with men," American Journal of Public Health, American Public Health Association, vol. 104(9), pages 1707-1712.
    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. repec:aph:ajpbhl:10.2105/ajph.2016.303512_4 is not listed on IDEAS
    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. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. Lutz Bornmann & Robin Haunschild & Vanash M Patel, 2020. "Are papers addressing certain diseases perceived where these diseases are prevalent? The proposal to use Twitter data as social-spatial sensors," PLOS ONE, Public Library of Science, vol. 15(11), pages 1-22, November.
    7. Muhammad Imran & Umair Qazi & Ferda Ofli, 2022. "TBCOV: Two Billion Multilingual COVID-19 Tweets with Sentiment, Entity, Geo, and Gender Labels," Data, MDPI, vol. 7(1), pages 1-27, January.
    8. Nason Maani Hessari & May CI van Schalkwyk & Sian Thomas & Mark Petticrew, 2019. "Alcohol Industry CSR Organisations: What Can Their Twitter Activity Tell Us about Their Independence and Their Priorities? A Comparative Analysis," IJERPH, MDPI, vol. 16(5), pages 1-12, March.
    9. Marcus Vinicius Santos & Fernando Morgado-Dias & Thiago C. Silva, 2023. "Oil Sector and Sentiment Analysis—A Review," Energies, MDPI, vol. 16(12), pages 1-29, June.
    10. Sonja I. Garske & Suzanne Elayan & Martin Sykora & Tamar Edry & Linus B. Grabenhenrich & Sandro Galea & Sarah R. Lowe & Oliver Gruebner, 2021. "Space-Time Dependence of Emotions on Twitter after a Natural Disaster," IJERPH, MDPI, vol. 18(10), pages 1-13, May.
    11. Irina Radu & Mandy Scheermesser & Martina Rebekka Spiess & Christina Schulze & Daniela Händler-Schuster & Jessica Pehlke-Milde, 2023. "Digital Health for Migrants, Ethnic and Cultural Minorities and the Role of Participatory Development: A Scoping Review," IJERPH, MDPI, vol. 20(20), pages 1-31, October.
    12. Santoveña-Casal, Sonia & Pérez, Ma Dolores Fernández, 2022. "Relevance of E-Participation in the state health campaign in Spain: #EstoNoEsUnJuego / #ThisIsNotAGame," Technology in Society, Elsevier, vol. 68(C).
    13. Malik, Aqdas & Berggren, Walter & Al-Busaidi, Adil S., 2022. "Instagram as a research tool for examining tobacco-related content: A methodological review," Technology in Society, Elsevier, vol. 70(C).
    14. Diane Ezeh Aruah & Yvonne Henshaw & Kim Walsh-Childers, 2023. "Tweets That Matter: Exploring the Solutions to Maternal Mortality in the United States Discussed by Advocacy Organizations on Twitter," IJERPH, MDPI, vol. 20(9), pages 1-14, April.
    15. Paramveer S. Dhillon & Sinan Aral, 2021. "Modeling Dynamic User Interests: A Neural Matrix Factorization Approach," Marketing Science, INFORMS, vol. 40(6), pages 1059-1080, November.
    16. Charalampos Ntompras & George Drosatos & Eleni Kaldoudi, 2022. "A high-resolution temporal and geospatial content analysis of Twitter posts related to the COVID-19 pandemic," Journal of Computational Social Science, Springer, vol. 5(1), pages 687-729, May.
    17. 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.
    18. Gabrielle Turner-McGrievy & Amir Karami & Courtney Monroe & Heather M. Brandt, 2020. "Dietary pattern recognition on Twitter: a case example of before, during, and after four natural disasters," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 103(1), pages 1035-1049, August.
    19. Miguel Angel Alvarez-Mon & Cesar I. Fernandez-Lazaro & Maria Llavero-Valero & Melchor Alvarez-Mon & Samia Mora & Miguel A. Martínez-González & Maira Bes-Rastrollo, 2022. "Mediterranean Diet Social Network Impact along 11 Years in the Major US Media Outlets: Thematic and Quantitative Analysis Using Twitter," IJERPH, MDPI, vol. 19(2), pages 1-16, January.
    20. Shelton, Rachel C. & Lee, Matthew & Brotzman, Laura E. & Crookes, Danielle M. & Jandorf, Lina & Erwin, Deborah & Gage-Bouchard, Elizabeth A., 2019. "Use of social network analysis in the development, dissemination, implementation, and sustainability of health behavior interventions for adults: A systematic review," Social Science & Medicine, Elsevier, vol. 220(C), pages 81-101.

    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:jdataj:v:4:y:2019:i:2:p:84-:d:238501. 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.