Health Information Technology Trends in Social Media: Using Twitter Data

Lee, Jisan; Kim, Jeongeun; Hong, Yeong Joo; Piao, Meihua; Byun, Ahjung; Song, Healim; Lee, Hyeong Suk

doi:10.4258/hir.2019.25.2.99

Cited by 21 publications

(13 citation statements)

References 17 publications

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“…The cluster network shows that natural language processing is connected with consumer health vocabulary , state , online support groups , and text . An examination of the core literature corresponding to the cluster shows that most scholars mined and dealt with the health-related contents of consumers through social media, health websites, health forums, and other ways to obtain the users’ health needs, explore the users’ health behaviors, and investigate the application of health information technology [ 61 , 62 ].…”

Section: Resultsmentioning

confidence: 99%

Evolutionary Overview of Consumer Health Informatics: Bibliometric Study on the Web of Science from 1999 to 2019

Ouyang¹,

Xie²,

Xin³

et al. 2021

J Med Internet Res

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Background Consumer health informatics (CHI) originated in the 1990s. With the rapid development of computer and information technology for health decision making, an increasing number of consumers have obtained health-related information through the internet, and CHI has also attracted the attention of an increasing number of scholars. Objective The aim of this study was to analyze the research themes and evolution characteristics of different study periods and to discuss the dynamic evolution path and research theme rules in a time-series framework from the perspective of a strategy map and a data flow in CHI. Methods The Web of Science core collection database of the Institute for Scientific Information was used as the data source to retrieve relevant articles in the field of CHI. SciMAT was used to preprocess the literature data and construct the overlapping map, evolution map, strategic diagram, and cluster network characterized by keywords. Besides, a bibliometric analysis of the general characteristics, the evolutionary characteristics of the theme, and the evolutionary path of the theme was conducted. Results A total of 986 articles were obtained after the retrieval, and 931 articles met the document-type requirement. In the past 21 years, the number of articles increased every year, with a remarkable growth after 2015. The research content in 4 different study periods formed the following 38 themes: patient education, medicine, needs, and bibliographic database in the 1999-2003 study period; world wide web, patient education, eHealth, patients, medication, terminology, behavior, technology, and disease in the 2004-2008 study period; websites, information seeking, physicians, attitudes, technology, risk, food labeling, patient, strategies, patient education, and eHealth in the 2009-2014 study period; and electronic medical records, health information seeking, attitudes, health communication, breast cancer, health literacy, technology, natural language processing, user-centered design, pharmacy, academic libraries, costs, internet utilization, and online health information in the 2015-2019 study period. Besides, these themes formed 10 evolution paths in 3 research directions: patient education and intervention, consumer demand attitude and behavior, and internet information technology application. Conclusions Averaging 93 publications every year since 2015, CHI research is in a rapid growth period. The research themes mainly focus on patient education, health information needs, health information search behavior, health behavior intervention, health literacy, health information technology, eHealth, and other aspects. Patient education and intervention research, consumer demand, attitude, and behavior research comprise the main theme evolution path, whose evolution process has been relatively stable. This evolution path will continue to become the research hotspot in this field. Research on the internet and information technology application is a secondary theme evolution path with development potential.

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Section: Resultsmentioning

confidence: 99%

Evolutionary Overview of Consumer Health Informatics: Bibliometric Study on the Web of Science from 1999 to 2019

Ouyang¹,

Xie²,

Xin³

et al. 2021

J Med Internet Res

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“…Twitter is a free microblogging service that enables its users to post, read, and respond to each other’s “tweets” (ie, short messages limited to 280 characters). Social media data are being increasingly used as a data source to inform health-related research, with the potential for offering a more efficient means of data collection over traditional, time-consuming, and costly survey-based methods [ 5 ]. In particular, Twitter has been used to monitor, track trends, and disseminate health information during past viral pandemics [ 6 - 9 ].…”

Section: Introductionmentioning

confidence: 99%

Using Natural Language Processing to Explore Mental Health Insights From UK Tweets During the COVID-19 Pandemic: Infodemiology Study

Marshall¹,

Lanyi²,

Green³

et al. 2022

JMIR Infodemiology

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Background There is need to consider the value of soft intelligence, leveraged using accessible natural language processing (NLP) tools, as a source of analyzed evidence to support public health research outputs and decision-making. Objective The aim of this study was to explore the value of soft intelligence analyzed using NLP. As a case study, we selected and used a commercially available NLP platform to identify, collect, and interrogate a large collection of UK tweets relating to mental health during the COVID-19 pandemic. Methods A search strategy comprised of a list of terms related to mental health, COVID-19, and lockdown restrictions was developed to prospectively collate relevant tweets via Twitter’s advanced search application programming interface over a 24-week period. We deployed a readily and commercially available NLP platform to explore tweet frequency and sentiment across the United Kingdom and identify key topics of discussion. A series of keyword filters were used to clean the initial data retrieved and also set up to track specific mental health problems. All collated tweets were anonymized. Results We identified and analyzed 286,902 tweets posted from UK user accounts from July 23, 2020 to January 6, 2021. The average sentiment score was 50%, suggesting overall neutral sentiment across all tweets over the study period. Major fluctuations in volume (between 12,622 and 51,340) and sentiment (between 25% and 49%) appeared to coincide with key changes to any local and/or national social distancing measures. Tweets around mental health were polarizing, discussed with both positive and negative sentiment. Key topics of consistent discussion over the study period included the impact of the pandemic on people’s mental health (both positively and negatively), fear and anxiety over lockdowns, and anger and mistrust toward the government. Conclusions Using an NLP platform, we were able to rapidly mine and analyze emerging health-related insights from UK tweets into how the pandemic may be impacting people’s mental health and well-being. This type of real-time analyzed evidence could act as a useful intelligence source that agencies, local leaders, and health care decision makers can potentially draw from, particularly during a health crisis.

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“…Twitter users share personal opinions and experiences by posting 280-character messages known as Tweets. Twitter has been increasingly used as a data source for health-related research, offering a more e cient means of data collection over traditional survey methods [2].For instance, it has been used to examine disease stigma [3], and to monitor disease pandemics [4] [5]. More recent studies have used Twitter data for the assessment of public sentiments, attitudes and opinions about health related issues [6] [7].…”

Section: Introductionmentioning

confidence: 99%

An exploratory analysis of public opinion and sentiments towards COVID-19 pandemic using Twitter data.

Chukwusa

Johnson

Gao

2020

Preprint

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Objective: Twitter data have been increasingly used to address health-related issues. However, little is known about their potential for understanding public opinions and sentiments of the current COVID-19 pandemic. The present study explores public opinion and sentiments about the COVID-19 pandemic using Tweets from 3 popular Coronavirus-related hashtags (#COVID19, #Coronavirus, #SARSCoV2).Results: Of the 39,726 Tweets analysed, we found that over 60% of words used within Tweets in all hashtags (#COVID19, 63.9%; #Coronavirus: 65.6%; #SARSCoV2: 63.5%) conveyed a negative mood towards the pandemic. Our results also showed similar trends in Tweet volume in #COVID19 and #SARSCoV2, with a spike in the number of Tweets on the 3rd and 6th of April 2020. Further exploration of Tweets in both hashtags revealed similar Twitter discussions related to topics on “Hydroxychloroquine” and “Hospitalisations of the British Prime minister” and “ the attainment of 1 million cases of coronavirus globally”.The findings of this exploratory study indicate that there is potential for using data generated from Twitter to understand general public opinion and sentiments towards the COVID-19 pandemic. However, caution is needed due to several limitations in this study. It is also important for future studies to explore the context around Tweets.

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Health Information Technology Trends in Social Media: Using Twitter Data

Cited by 21 publications

References 17 publications

Evolutionary Overview of Consumer Health Informatics: Bibliometric Study on the Web of Science from 1999 to 2019

Evolutionary Overview of Consumer Health Informatics: Bibliometric Study on the Web of Science from 1999 to 2019

Using Natural Language Processing to Explore Mental Health Insights From UK Tweets During the COVID-19 Pandemic: Infodemiology Study

An exploratory analysis of public opinion and sentiments towards COVID-19 pandemic using Twitter data.

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