Automatic Detection of Twitter Users Who Express Chronic Stress Experiences via Supervised Machine Learning and Natural Language Processing.
Americans bear a high chronic stress burden, particularly during the COVID-19 pandemic. Although social media have many strengths to complement the weaknesses of conventional stress measures, including surveys, they have been rarely utilized to detect individuals self-reporting chronic stress. Thus,...
| Publicado en: | CIN: Computers, Informatics, Nursing Vol. 41; no. 9; pp. 717 - 725 |
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| Autores principales: | , , , , , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Lippincott Williams & Wilkins
Sep2023
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=190995977&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 190995977 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 15382931 KXN jtl: CIN: Computers, Informatics, Nursing issn: 15382931 maglogo: N pubinfo: dt: Sep2023 vid: 41 iid: 9 pid: 433 pub: Lippincott Williams & Wilkins place: Baltimore, Maryland artinfo: ui: 190995977 190995977 190995977 10.1097/CIN.0000000000000985 190995977 ppf: 717 ppct: 8 formats: tig: atl: Automatic Detection of Twitter Users Who Express Chronic Stress Experiences via Supervised Machine Learning and Natural Language Processing. aug: au: Yuan-Chi Yang Xie, Angel Kim, Sangmi Hair, Jessica Al-Garadi, Mohammed Sarker, Abeed affil: Department of Biomedical Informatics, School of Medicine, Emory University, Atlanta, GA sug: subj: Machine Learning Algorithms Self Report Chronic Disease Stress, Psychological Natural Language Processing Automation Diagnosis, Computer Assisted Systems Development Consumers Psychosocial Factors Detection Algorithms Classification Algorithms Human User-Computer Interface kappa Statistic Descriptive Statistics Interrater Reliability Sensitivity and Specificity Pilot Studies Funding Source Interviews Content Analysis Support Vector Machine Random Forest Neural Networks (Computer) Precision Confidence Intervals Information Retrieval Exploratory Research Data Mining ab: Americans bear a high chronic stress burden, particularly during the COVID-19 pandemic. Although social media have many strengths to complement the weaknesses of conventional stress measures, including surveys, they have been rarely utilized to detect individuals self-reporting chronic stress. Thus, this study aimed to develop and evaluate an automatic system on Twitter to identify users who have self-reported chronic stress experiences. Using the Twitter public streaming application programming interface, we collected tweets containing certain stress-related keywords (eg, "chronic," "constant," "stress") and then filtered the data using pre-defined text patterns. We manually annotated tweets with (without) self-report of chronic stress as positive (negative). We trained multiple classifiers and tested them via accuracy and 퐹1 score. We annotated 4195 tweets (1560 positives, 2635 negatives), achieving an inter-annotator agreement of 0.83 (Cohen's kappa). The classifier based on Bidirectional Encoder Representation from Transformers performed the best (accuracy of 83.6% [81.0- 86.1]), outperforming the second best-performing classifier (support vector machines: 76.4% [73.5-79.3]). The past tweets from the authors of positive tweets contained useful information, including sources and health impacts of chronic stress. Our study demonstrates that users' self-reported chronic stress experiences can be automatically identified on Twitter, which has a high potential for surveillance and large-scale intervention. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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