Analysis of tweets regarding psychological disorders before and during the COVID-19 pandemic: The case of Turkey.
This study aimed to examine the effects of the COVID-19 pandemic on Turkish society in relation to obsessive-compulsive disorder, anxiety disorder, and depression via content mining of tweets. Tweets were obtained by searching selected keywords via Twitter application programming interface in Python...
| Publicado en: | Digital Scholarship in the Humanities Vol. 37; no. 4; pp. 1269 - 1281 |
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| Autores principales: | , , |
| Formato: | Artículo |
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Oxford University Press / USA
Dec2022
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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=hlh&AN=159850230&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 159850230 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 2055768X JEO9 jtl: Digital Scholarship in the Humanities issn: 2055768X maglogo: N pubinfo: dt: Dec2022 vid: 37 iid: 4 pid: 622 pub: Oxford University Press / USA artinfo: ui: 159850230 10.1093/llc/fqab102 ppf: 1269 ppct: 12 formats: fmt: – @attributes: type: T – @attributes: type: P size: 404KB tig: atl: Analysis of tweets regarding psychological disorders before and during the COVID-19 pandemic: The case of Turkey. aug: au: Tankut, Ülkü Esen, M Fevzi Balaban, Gülşah affil: Department of Psychology, University of Health Sciences , Turkey Department of Healthcare Information Systems, University of Health Sciences , Turkey Department of Psychology, İstanbul Sabahattin Zaim University , Turkey su: Pandemics COVID-19 pandemic Content mining Obsessive-compulsive disorder Keyword searching Anxiety disorders Deep brain stimulation Türkiye sug: subj: Türkiye Pandemics COVID-19 pandemic Content mining Obsessive-compulsive disorder Keyword searching Anxiety disorders Deep brain stimulation ab: This study aimed to examine the effects of the COVID-19 pandemic on Turkish society in relation to obsessive-compulsive disorder, anxiety disorder, and depression via content mining of tweets. Tweets were obtained by searching selected keywords via Twitter application programming interface in Python. The tweets were then filtered for psychopathology-related keywords. The sample consisted of 65,031 publicly available tweets that cover the period between 2 December 2019 and 31 May 2021. Latent Dirichlet allocation, was performed to uncover the latent semantic structures in the tweets. Data transformation and analysis were performed by using open-source R (version 4.0.2). As a result of the analysis, there were statistically significant differences in the total number of tweets, mean number of comments, likes, and retweets per tweet between the pre-pandemic and pandemic periods. From the topic modeling, it was also found that semantic strings of the tweets differed in the pandemic period compared to the pre-pandemic period. Topic analysis of social media shares can provide information on the mental health conditions of individuals and the use of tweet content can contribute to the research of psychopathologies, especially during the pandemic. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: © 2019 EADH: The European Association for Digital Humanities. item: Digital Scholarship in the Humanities holder: Oxford University Press / USA dt: @attributes: year: 2022 holdings: @attributes: islocal: N |
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