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A New Tool for Public Health Opinion to Give Insight Into Telemedicine: Twitter Poll Analysis

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submitted on 2024-05-28, 06:55 and posted on 2024-05-28, 06:56 authored by Josep Vidal-Alaball, Luis Fernandez-Luque, Francesc X Marin-Gomez, Wasim Ahmed


Telemedicine draws on information technologies in order to enable the delivery of clinical health care from a distance. Twitter is a social networking platform that has 316 million monthly active users with 500 million tweets per day; its potential for real-time monitoring of public health has been well documented. There is a lack of empirical research that has critically examined the potential of Twitter polls for providing insight into public health. One of the benefits of utilizing Twitter polls is that it is possible to gain access to a large audience that can provide instant and real-time feedback. Moreover, Twitter polls are completely anonymized.


The overall aim of this study was to develop and disseminate Twitter polls based on existing surveys to gain real-time feedback on public views and opinions toward telemedicine.


Two Twitter polls were developed utilizing questions from previously used questionnaires to explore acceptance of telemedicine among Twitter users. The polls were placed on the Twitter timeline of one of the authors, which had more than 9300 followers, and the account followers were asked to answer the poll and retweet it to reach a larger audience.


In a population where telemedicine was expected to enjoy big support, a significant number of Twitter users responding to the poll felt that telemedicine was not as good as traditional care.


Our results show the potential of Twitter polls for gaining insight into public health topics on a range of health issues not just limited to telemedicine. Our study also sheds light on how Twitter polls can be used to validate and test survey questions.

JMIR Form Res 2019;3(2):e13870

Other Information

Published in: JMIR Formative Research
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Open Access funding provided by the Qatar National Library.



  • English


JMIR Publications

Publication Year

  • 2019

License statement

This Item is licensed under the Creative Commons Attribution 4.0 International License

Institution affiliated with

  • Hamad Bin Khalifa University
  • Qatar Computing Research Institute - HBKU

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