Healthc Inform Res.  2019 Apr;25(2):99-105. 10.4258/hir.2019.25.2.99.

Health Information Technology Trends in Social Media: Using Twitter Data

• Lee J
• Kim J
• Hong YJ
• Piao M
• Byun A
• Song H
• Lee HS

• KMID: 2445006
• DOI: http://doi.org/10.4258/hir.2019.25.2.99

Abstract

OBJECTIVES
This study analyzed the health technology trends and sentiments of users using Twitter data in an attempt to examine the public's opinions and identify their needs.
METHODS
Twitter data related to health technology, from January 2010 to October 2016, were collected. An ontology related to health technology was developed. Frequently occurring keywords were analyzed and visualized with the word cloud technique. The keywords were then reclassified and analyzed using the developed ontology and sentiment dictionary. Python and the R program were used for crawling, natural language processing, and sentiment analysis.
RESULTS
In the developed ontology, the keywords are divided into "˜health technology"˜ and "˜health information"˜. Under health technology, there are are six subcategories, namely, health technology, wearable technology, biotechnology, mobile health, medical technology, and telemedicine. Under health information, there are four subcategories, namely, health information, privacy, clinical informatics, and consumer health informatics. The number of tweets about health technology has consistently increased since 2010; the number of posts in 2014 was double that in 2010, which was about 150 thousand posts. Posts about mHealth accounted for the majority, and the dominant words were "˜care"˜, "˜new"˜, "˜mental"˜, and "˜fitness"˜. Sentiment analysis by subcategory showed that most of the posts in nearly all subcategories had a positive tone with a positive score.
CONCLUSIONS
Interests in mHealth have risen recently, and consequently, posts about mHealth were the most frequent. Examining social media users' responses to new health technology can be a useful method to understand the trends in rapidly evolving fields.

Keyword

Consumer Health Informatics; Data Mining; Natural Language Processing; Social Media; Public Opinion

MeSH Terms

Biomedical Technology
Biotechnology
Boidae
Data Mining
Informatics
Medical Informatics*
Methods
Natural Language Processing
Privacy
Public Opinion
Social Media*
Telemedicine

Figure

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  Figure 1 Keywords retrieval process.

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  Figure 2 Frequency of Twitter posting of categories.

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  Figure 3 Word clouds of Twitter posts related to health technology.

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