ArticleJournal of medical Internet research2015
Characterizing Sleep Issues Using Twitter.
Article in Journal of medical Internet research, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
38 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A systematic literature review of machine learning in online personal health data.Journal of the American Medical Informatics Association : JAMIA · 2019Pooled it
- Large-scale patterns of self-reported mood and sleep across the day and week on an online cognitive-training platform.PLOS digital health · 2026Article
- Outdoor Artificial Light at Night and Insomnia-Related Social Media Posts.JAMA network open · 2024Article
- Inferring sleep disturbance from text messages of suicide attempt survivors: A pilot study.Suicide & life-threatening behavior · 2023Article
- Estimation of Bedtimes of Reddit Users: Integrated Analysis of Time Stamps and Surveys.JMIR formative research · 2023Article
- Identifying Sleep Disorders From Search Engine Activity: Combining User-Generated Data With a Clinically Validated Questionnaire.Journal of medical Internet research · 2022Article
- Crowdsourcing for Machine Learning in Public Health Surveillance: Lessons Learned From Amazon Mechanical Turk.Journal of medical Internet research · 2022Article
- Lessons for science and technology policy? Probing the Linkedin network of an RDI organisation.SN social sciences · 2022Article
- Sentiment analysis of tweets on alopecia areata, hidradenitis suppurativa, and psoriasis: Revealing the patient experience.Frontiers in medicine · 2022Article
- Identifying Insomnia From Social Media Posts: Psycholinguistic Analyses of User Tweets.Journal of medical Internet research · 2021Article
- Exploring discussions of health and risk and public sentiment in Massachusetts during COVID-19 pandemic mandate implementation: A Twitter analysis.SSM - population health · 2021Article
- Consumer Views on Health Applications of Consumer Digital Data and Health Privacy Among US Adults: Qualitative Interview Study.Journal of medical Internet research · 2021Article
- Negative Perception of the COVID-19 Pandemic Is Dropping: Evidence From Twitter Posts.Frontiers in psychology · 2021Article
- Psychosocial phenotyping as a personalization strategy for chronic disease self-management interventions.American journal of translational research · 2021Article
- Twitter-Derived Social Neighborhood Characteristics and Individual-Level Cardiometabolic Outcomes: Cross-Sectional Study in a Nationally Representative Sample.JMIR public health and surveillance · 2020Article
- Using Twitter to Surveil the Opioid Epidemic in North Carolina: An Exploratory Study.JMIR public health and surveillance · 2020Article
- Estimating geographic subjective well-being from Twitter: A comparison of dictionary and data-driven language methods.Proceedings of the National Academy of Sciences of the United States of America · 2020Article
- Are Aspects of Twitter Use Associated with Reduced Depressive Symptoms? The Moderating Role of In-Person Social Support.Cyberpsychology, behavior and social networking · 2019Article
- Extracting health-related causality from twitter messages using natural language processing.BMC medical informatics and decision making · 2019Article
- Feasibility of using social media to monitor outdoor air pollution in London, England.Preventive medicine · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
backgroundSleep issues such as insomnia affect over 50 million Americans and can lead to serious health problems, including depression and obesity, and can increase risk of injury. Social media platforms such as Twitter offer exciting potential for their use in studying and identifying both diseases and social phenomenon.
objectiveOur aim was to determine whether social media can be used as a method to conduct research focusing on sleep issues.
methodsTwitter posts were collected and curated to determine whether a user exhibited signs of sleep issues based on the presence of several keywords in tweets such as insomnia, "can't sleep", Ambien, and others. Users whose tweets contain any of the keywords were designated as having self-identified sleep issues (sleep group). Users who did not have self-identified sleep issues (non-sleep group) were selected from tweets that did not contain pre-defined words or phrases used as a proxy for sleep issues.
resultsUser data such as number of tweets, friends, followers, and location were collected, as well as the time and date of tweets. Additionally, the sentiment of each tweet and average sentiment of each user were determined to investigate differences between non-sleep and sleep groups. It was found that sleep group users were significantly less active on Twitter (P=.04), had fewer friends (P<.001), and fewer followers (P<.001) compared to others, after adjusting for the length of time each user's account has been active. Sleep group users were more active during typical sleeping hours than others, which may suggest they were having difficulty sleeping. Sleep group users also had significantly lower sentiment in their tweets (P<.001), indicating a possible relationship between sleep and pyschosocial issues.
conclusionsWe have demonstrated a novel method for studying sleep issues that allows for fast, cost-effective, and customizable data to be gathered.
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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.