| Autor: |
Keisuke Nakagawa, Nuen Tsang Yang, Machelle Wilson, Peter Yellowlees |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
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| Zdroj: |
Journal of Medical Internet Research, Vol 24, Iss 9, p e37752 (2022) |
| Druh dokumentu: |
article |
| ISSN: |
1438-8871 |
| DOI: |
10.2196/37752 |
| Popis: |
BackgroundPhysicians are increasingly using Twitter as a channel for communicating with colleagues and the public. Identifying physicians on Twitter is difficult due to the varied and imprecise ways that people self-identify themselves on the social media platform. This is the first study to describe a reliable, repeatable methodology for identifying physicians on Twitter. By using this approach, we characterized the longitudinal activity of US physicians on Twitter. ObjectiveWe aimed to develop a reliable and repeatable methodology for identifying US physicians on Twitter and to characterize their activity on Twitter over 5 years by activity, tweeted topic, and account type. MethodsIn this study, 5 years of Twitter data (2016-2020) were mined for physician accounts. US physicians on Twitter were identified by using a custom-built algorithm to screen for physician identifiers in the Twitter handles, user profiles, and tweeted content. The number of tweets by physician accounts from the 5-year period were counted and analyzed. The top 100 hashtags were identified, categorized into topics, and analyzed. ResultsApproximately 1 trillion tweets were mined to identify 6,399,146 ( |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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