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KMID : 1100520190250020099
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Healthcare Informatics Research
2019 Volume.25 No. 2 p.99 ~ p.105
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Health Information Technology Trends in Social Media: Using Twitter Data
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Lee Ji-San
Kim Jeong-Eun
Hong Yeong-Joo
Piao Meihua
Byun Ah-Jung
Song Hea-Lim
Lee Hyeong-Suk
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Abstract
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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.
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KEYWORD
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Consumer Health Informatics, Data Mining, Natural Language Processing, Social Media, Public Opinion
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