ArticleJournal of diabetes science and technology2019
Diabetes on Twitter: A Sentiment Analysis.
Article in Journal of diabetes science and technology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
What it found
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Who cites it
29 citing papers in PubMed.
- Examining contraception-related discourse on social media after the Dobbs v. Jackson Women's Health Organization Supreme Court decision: a textual analysis of user-generated content on X (formerly Twitter).Reproductive health · 2026Article
- Evaluating Turkish society's approaches to hemorrhoid treatment using text mining and sentiment analysis methods.Frontiers in surgery · 2026Article
- Whose tweets about obesity and weight loss gain the most attention: celebrities, political, or medical authorities?International journal of obesity (2005) · 2025Article
- Understanding the Engagement and Interaction of Superusers and Regular Users in UK Respiratory Online Health Communities: Deep Learning-Based Sentiment Analysis.Journal of medical Internet research · 2025Article
- Promoting Comprehensive Sexuality Education in Pakistan Using a Cocreated Social Media Intervention: Development and Pilot Testing Study.JMIR formative research · 2024Article
- Analyzing Patient Experience on Weibo: Machine Learning Approach to Topic Modeling and Sentiment Analysis.JMIR medical informatics · 2024Article
- Predicting hospital length of stay using machine learning on a large open health dataset.BMC health services research · 2024Article
- Topic modeling and social network analysis approach to explore diabetes discourse on Twitter in India.Frontiers in artificial intelligence · 2024Article
- What Do Students' Questionnaire Responses Tell Us about Their Language around Person-Centred Care? An Exploratory Sentiment Analysis.Healthcare (Basel, Switzerland) · 2023Article
- School-based management of type 1 diabetes in the Republic of Ireland: a cross-sectional mixed-methods analysis.Irish journal of medical science · 2023Article
- Use of Social Media as a Platform for Education and Support for People With Diabetes During a Global Pandemic.Journal of diabetes science and technology · 2023Article
- Mapping obesity and diabetes' representation on Twitter: the case of Italy.Frontiers in sociology · 2023Article
- What are IBD Patients Talking About on Twitter? Using Natural Language Understanding to Investigate Patients' Tweets.SN computer science · 2023Article
- Twitter Usage Among Physicians From 2016 to 2020: Algorithm Development and Longitudinal Analysis Study.Journal of medical Internet research · 2022Article
- Identifying Patients With Inflammatory Bowel Disease on Twitter and Learning From Their Personal Experience: Retrospective Cohort Study.Journal of medical Internet research · 2022Article
- Attitudes and Expectations of Clinical Research Participants Toward Digital Health and Mobile Dietary Assessment Tools: Cross-Sectional Survey Study.Frontiers in digital health · 2022Article
- Sentiment analysis of tweets on alopecia areata, hidradenitis suppurativa, and psoriasis: Revealing the patient experience.Frontiers in medicine · 2022Article
- Perceptions of a short animated film on adverse childhood experiences: a mixed methods evaluation.BMJ open · 2021Article
- Machine learning in medicine: a practical introduction to natural language processing.BMC medical research methodology · 2021Article
- Analyzing Twitter Data to Evaluate People's Attitudes towards Public Health Policies and Events in the Era of COVID-19.International journal of environmental research and public health · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundContents published on social media have an impact on individuals and on their decision making. Knowing the sentiment toward diabetes is fundamental to understanding the impact that such information could have on people affected with this health condition and their family members. The objective of this study is to analyze the sentiment expressed in messages on diabetes posted on Twitter.
methodTweets including one of the terms "diabetes," "t1d," and/or "t2d" were extracted for one week using the Twitter standard API. Only the text message and the number of followers of the users were extracted. The sentiment analysis was performed by using SentiStrength.
resultsA total of 67 421 tweets were automatically extracted, of those 3.7% specifically referred to T1D; and 6.8% specifically mentioned T2D. One or more emojis were included in 7.0% of the posts. Tweets specifically mentioning T2D and that did not include emojis were significantly more negative than the tweets that included emojis (-2.22 vs -1.48, P < .001). Tweets on T1D and that included emojis were both significantly more positive and also less negative than tweets without emojis (1.71 vs 1.49 and -1.31 vs -1.50, respectively; P < .005). The number of followers had a negative association with positive sentiment strength ( r = -.023, P < .001) and a positive association with negative sentiment ( r = .016, P < .001).
conclusionThe use of sentiment analysis techniques on social media could increase our knowledge of how social media impact people with diabetes and their families and could help to improve public health strategies.
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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.