Previously submitted to: JMIR Mental Health (no longer under consideration since Feb 25, 2024)
Date Submitted: Feb 22, 2024
Open Peer Review Period: Feb 25, 2024 - Feb 25, 2024
(closed for review but you can still tweet)
NOTE: This is an unreviewed Preprint
Warning: This is a unreviewed preprint (What is a preprint?). Readers are warned that the document has not been peer-reviewed by expert/patient reviewers or an academic editor, may contain misleading claims, and is likely to undergo changes before final publication, if accepted, or may have been rejected/withdrawn (a note "no longer under consideration" will appear above).
Peer review me: Readers with interest and expertise are encouraged to sign up as peer-reviewer, if the paper is within an open peer-review period (in this case, a "Peer Review Me" button to sign up as reviewer is displayed above). All preprints currently open for review are listed here. Outside of the formal open peer-review period we encourage you to tweet about the preprint.
Citation: Please cite this preprint only for review purposes or for grant applications and CVs (if you are the author).
Final version: If our system detects a final peer-reviewed "version of record" (VoR) published in any journal, a link to that VoR will appear below. Readers are then encourage to cite the VoR instead of this preprint.
Settings: If you are the author, you can login and change the preprint display settings, but the preprint URL/DOI is supposed to be stable and citable, so it should not be removed once posted.
Submit: To post your own preprint, simply submit to any JMIR journal, and choose the appropriate settings to expose your submitted version as preprint.
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Corpus Analysis of Coronavirus Disease-19 (COVID-19)-Related Loneliness in Twitter
ABSTRACT
Background:
Loneliness is a complex mental health issue involving feelings of isolation, disconnectedness, and a lack of purpose in life. The coronavirus disease (COVID-19) pandemic and lockdowns significantly increased social isolation, contributing to a global rise in loneliness on social online platforms. However, research on mental health expressions in Arabic on social media is limited, exacerbating the lack of understanding of loneliness and mental health issues in these communities. Stigma and societal resistance in the Middle East hinder individuals from seeking mental health support, a trend which was particularly noticeable amidst the challenges of the pandemic.
Objective:
This study aimed to conduct a corpus analysis of an open-source dataset to gain insights into the origin of how loneliness was expressed in Arabic on Twitter during the initial months of COVID-19 regulations. Additionally, it explored temporal trends during the same time period to enhance comprehension of the prevalence of loneliness and mental health struggles during the initial months of the pandemic.
Methods:
Grounded in corpus linguistic methodology, the study also employs thematic analysis, explores temporal trends, and conducts Inter-Annotator Agreement (IAA) to comprehend the prevalence of loneliness and mental health struggles during the pandemic's early stages.
Results:
Our analysis of Twitter data using AntConc revealed crucial insights into the origins of mental health challenges and loneliness during the early days of the COVID-19 pandemic. Employing sophisticated linguistic techniques, we identified over 18,000 instances of loneliness-related terms, which deepened our understanding of the issue. Emotions were expressed through various themes, with a significant focus on COVID-19 news and opinions. The advanced search features of AntConc were instrumental in this process, allowing us to gauge word frequency and locate relevant tweets. The association between loneliness and the pandemic was clear, especially with the word "corona" being the most frequent collocate. Thematic analysis identified the seven key themes that were despair in the face of uncertainty, grief for the loss of life, hope through faith, opinions on COVID-19, opinions on isolation, COVID-19 news, and fear of illness which characterized the emotional landscape during this time. Inter-Annotator Agreement highlighted the diversity of interpretations.
Conclusions:
Our study demonstrates the crucial role social media plays in capturing and understanding human emotions during major global events like the COVID-19 pandemic. Our AntConc analysis offers insights into mental health and loneliness during the early COVID-19 pandemic. It highlights the Arabic-speaking online community's experiences and contributes to global understanding of mental health and social media dynamics. This approach can be applied to different languages, enriching our understanding of loneliness and mental health during crises.
Citation
Request queued. Please wait while the file is being generated. It may take some time.
Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.