IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0112235.html
   My bibliography  Save this article

Engagement with Health Agencies on Twitter

Author

Listed:
  • Sanmitra Bhattacharya
  • Padmini Srinivasan
  • Phil Polgreen

Abstract

Objective: To investigate factors associated with engagement of U.S. Federal Health Agencies via Twitter. Our specific goals are to study factors related to a) numbers of retweets, b) time between the agency tweet and first retweet and c) time between the agency tweet and last retweet. Methods: We collect 164,104 tweets from 25 Federal Health Agencies and their 130 accounts. We use negative binomial hurdle regression models and Cox proportional hazards models to explore the influence of 26 factors on agency engagement. Account features include network centrality, tweet count, numbers of friends, followers, and favorites. Tweet features include age, the use of hashtags, user-mentions, URLs, sentiment measured using Sentistrength, and tweet content represented by fifteen semantic groups. Results: A third of the tweets (53,556) had zero retweets. Less than 1% (613) had more than 100 retweets (mean = 284). The hurdle analysis shows that hashtags, URLs and user-mentions are positively associated with retweets; sentiment has no association with retweets; and tweet count has a negative association with retweets. Almost all semantic groups, except for geographic areas, occupations and organizations, are positively associated with retweeting. The survival analyses indicate that engagement is positively associated with tweet age and the follower count. Conclusions: Some of the factors associated with higher levels of Twitter engagement cannot be changed by the agencies, but others can be modified (e.g., use of hashtags, URLs). Our findings provide the background for future controlled experiments to increase public health engagement via Twitter.

Suggested Citation

  • Sanmitra Bhattacharya & Padmini Srinivasan & Phil Polgreen, 2014. "Engagement with Health Agencies on Twitter," PLOS ONE, Public Library of Science, vol. 9(11), pages 1-12, November.
    • Handle: RePEc:plo:pone00:0112235
    DOI: 10.1371/journal.pone.0112235
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0112235
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0112235&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0112235?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
    ---><---

    References listed on IDEAS

    as
    1. Cameron,A. Colin & Trivedi,Pravin K., 2013. "Regression Analysis of Count Data," Cambridge Books, Cambridge University Press, number 9781107667273, January.
    2. Mike Thelwall & Kevan Buckley & Georgios Paltoglou, 2011. "Sentiment in Twitter events," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 62(2), pages 406-418, February.
    3. Mullahy, John, 1986. "Specification and testing of some modified count data models," Journal of Econometrics, Elsevier, vol. 33(3), pages 341-365, December.
    4. Mike Thelwall & Kevan Buckley & Georgios Paltoglou, 2011. "Sentiment in Twitter events," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 62(2), pages 406-418, February.
    5. Mike Thelwall & Kevan Buckley & Georgios Paltoglou & Di Cai & Arvid Kappas, 2010. "Sentiment strength detection in short informal text," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 61(12), pages 2544-2558, December.
    6. Mike Thelwall & Kevan Buckley & Georgios Paltoglou & Di Cai & Arvid Kappas, 2010. "Sentiment strength detection in short informal text," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 61(12), pages 2544-2558, December.
    7. Winkelmann, Rainer & Zimmermann, Klaus F, 1995. "Recent Developments in Count Data Modelling: Theory and Application," Journal of Economic Surveys, Wiley Blackwell, vol. 9(1), pages 1-24, March.
    8. Rajagopal, 2013. "Social Media Metrics," Palgrave Macmillan Books, in: Managing Social Media and Consumerism, chapter 7, pages 132-151, Palgrave Macmillan.
    9. 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.
    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. Zhichao Fang & Rodrigo Costas & Paul Wouters, 2022. "User engagement with scholarly tweets of scientific papers: a large-scale and cross-disciplinary analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(8), pages 4523-4546, August.

    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. Ma, Jie & Tse, Ying Kei & Wang, Xiaojun & Zhang, Minhao, 2019. "Examining customer perception and behaviour through social media research – An empirical study of the United Airlines overbooking crisis," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 127(C), pages 192-205.
    2. Lipizzi, Carlo & Iandoli, Luca & Ramirez Marquez, José Emmanuel, 2015. "Extracting and evaluating conversational patterns in social media: A socio-semantic analysis of customers’ reactions to the launch of new products using Twitter streams," International Journal of Information Management, Elsevier, vol. 35(4), pages 490-503.
    3. Ping-Yu Hsu & Hong-Tsuen Lei & Shih-Hsiang Huang & Teng Hao Liao & Yao-Chung Lo & Chin-Chun Lo, 2019. "Effects of sentiment on recommendations in social network," Electronic Markets, Springer;IIM University of St. Gallen, vol. 29(2), pages 253-262, June.
    4. Heleen Brans & Bert Scholtens, 2020. "Under his thumb the effect of president Donald Trump’s Twitter messages on the US stock market," PLOS ONE, Public Library of Science, vol. 15(3), pages 1-11, March.
    5. Oleg S. Nagornyy & Olessia Y. Koltsova, 2017. "Mining Media Topics Perceived as Social Problems by Online Audiences: Use of a Data Mining Approach in Sociology," HSE Working papers WP BRP 74/SOC/2017, National Research University Higher School of Economics.
    6. Sandra González-Bailón & Georgios Paltoglou, 2015. "Signals of Public Opinion in Online Communication," The ANNALS of the American Academy of Political and Social Science, , vol. 659(1), pages 95-107, May.
    7. Hajar Sotudeh & Zeinab Saber & Farzin Ghanbari Aloni & Mahdieh Mirzabeigi & Farshad Khunjush, 2022. "A longitudinal study of the evolution of opinions about open access and its main features: a twitter sentiment analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(10), pages 5587-5611, October.
    8. Christopher Courtney & Supradeep Dutta & Yong Li, 2017. "Resolving Information Asymmetry: Signaling, Endorsement, and Crowdfunding Success," Entrepreneurship Theory and Practice, , vol. 41(2), pages 265-290, March.
    9. 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.
    10. Wang, Fang & Du, Zhao & Wang, Shan, 2023. "Information multidimensionality in online customer reviews," Journal of Business Research, Elsevier, vol. 159(C).
    11. Daniel E. O'Leary, 2015. "Twitter Mining for Discovery, Prediction and Causality: Applications and Methodologies," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 22(3), pages 227-247, July.
    12. Mohammadreza Mousavizadeh & Mehrdad Koohikamali & Mohammad Salehan & Dam J. Kim, 2022. "An Investigation of Peripheral and Central Cues of Online Customer Review Voting and Helpfulness through the Lens of Elaboration Likelihood Model," Information Systems Frontiers, Springer, vol. 24(1), pages 211-231, February.
    13. Greene, William, 2007. "Functional Form and Heterogeneity in Models for Count Data," Foundations and Trends(R) in Econometrics, now publishers, vol. 1(2), pages 113-218, August.
    14. Niklas Elert, 2014. "What determines entry? Evidence from Sweden," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 53(1), pages 55-92, August.
    15. Christian Kleiber & Achim Zeileis, 2016. "Visualizing Count Data Regressions Using Rootograms," The American Statistician, Taylor & Francis Journals, vol. 70(3), pages 296-303, July.
    16. Diana Maynard & Gerhard Gossen & Adam Funk & Marco Fisichella, 2014. "Should I Care about Your Opinion? Detection of Opinion Interestingness and Dynamics in Social Media," Future Internet, MDPI, vol. 6(3), pages 1-25, August.
    17. J. M. C. Santos Silva & Silvana Tenreyro, 2022. "The Log of Gravity at 15," Portuguese Economic Journal, Springer;Instituto Superior de Economia e Gestao, vol. 21(3), pages 423-437, September.
    18. Chiara Bocci & Laura Grassini & Emilia Rocco, 2021. "A multiple inflated negative binomial hurdle regression model: analysis of the Italians’ tourism behaviour during the Great Recession," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 30(4), pages 1109-1133, October.
    19. Karin Sim Smith & Richard McCreadie & Craig Macdonald & Iadh Ounis, 2018. "Regional Sentiment Bias in Social Media Reporting During Crises," Information Systems Frontiers, Springer, vol. 20(5), pages 1013-1025, October.
    20. Beatriz Barros & Ana Fernández-Zubieta & Raul Fidalgo-Merino & Francisco Triguero, 2018. "Scientific knowledge percolation process and social impact: A case study on the biotechnology and microbiology perceptions on Twitter," Science and Public Policy, Oxford University Press, vol. 45(6), pages 804-814.

    More about this item

    Statistics

    Access and download statistics

    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:plo:pone00:0112235. 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: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    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.