ArticleHealthcare informatics research2019
Health Information Technology Trends in Social Media: Using Twitter Data.
Article in Healthcare informatics research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed.
- Smokers' Likelihood to Engage With Information and Misinformation on Twitter About the Relative Harms of e-Cigarette Use: Results From a Randomized Controlled Trial.JMIR public health and surveillance · 2021Trial
- Analysis of Telehealth Discussion Trends on Reddit (2019-2022).Telemedicine journal and e-health : the official journal of the American Telemedicine Association · 2024Article
- The Utilization of Natural Language Processing for Analyzing Social Media Data in Nursing Research: A Scoping Review.Journal of nursing management · 2024Article
- Looking at the fringes of MedTech innovation: a mapping review of horizon scanning and foresight methods.BMJ open · 2023Review
- Sentiment and emotion trends in nurses' tweets about the COVID-19 pandemic.Journal of nursing scholarship : an official publication of Sigma Theta Tau International Honor Society of Nursing · 2022Article
- Using social media listening and data mining to understand travellers' perspectives on travel disease risks and vaccine-related attitudes and behaviours.Journal of travel medicine · 2022Article
- Evolutionary Overview of Consumer Health Informatics: Bibliometric Study on the Web of Science from 1999 to 2019.Journal of medical Internet research · 2021Review
- Keyword Trends for Mother-Child Oral Health in Korea Based on Social Media Big Data from Naver.Healthcare informatics research · 2020Article
- Using Natural Language Processing to Explore Mental Health Insights From UK Tweets During the COVID-19 Pandemic: Infodemiology Study.JMIR infodemiologyArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesThis 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.
methodsTwitter 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.
resultsIn 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.
conclusionsInterests 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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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.