[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123897-en":3,"doc-seo-123897-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},123897,137441390410,"Hazel","https://ap-avatar.wpscdn.com/avatar/2000252f4ab5702993?_k=1776741390130283984",8,"Research & Report","Identifying COVID-19 survivors living with post-traumatic stress disorder through machine learning on Twitter","The COVID-19 pandemic disrupted daily life and brought widespread economic loss, while attention to its mental-health impact remained limited. This study addresses the lack of systematic work for monitoring and detecting post-traumatic stress disorder using social media signals. Classical machine learning models are trained to classify tweets as COVID-PTSD positive or negative. Multiple classifiers and feature-selection combinations are evaluated, demonstrating performance with 83.29% accuracy using Support Vector Machine and unigram features on a real-world dataset.","University of Birmingham  \nIdentifying COVID-19 survivors living with posttraumatic stress disorder through machine learning on Twitter  \nBaqir, Anees; Ali, Mubashir; Jaffar, Shaista; Sherazi, Hafiz Husnain Raza; Lee, Mark; Bashir, Ali Kashif; Al Dabel, Maryam M.  \nDOI:  \n10.1038/s41598-024-69687-8  \nLicense:  \nCreative Commons: Attribution (CC BY)  \nDocument Version  \nPublisher's PDF, also known as Version of record  \nCitation for published version (Harvard):  \nBaqir, A, Ali, M, Jaffar, S, Sherazi, HHR, Lee, M, Bashir, AK & Al Dabel, MM 2024, ' Identifying COVID-19 survivors living with post-traumatic stress disorder through machine learning on Twitter', Scientific Reports, vol.  \n14, no. 1, 18902. [https://doi.org/10.1038/s41598-024-69687-8](https://doi.org/10.1038/s41598-024-69687-8)  \nLink to publication on Research at Birmingham portal  \nGeneral rights  \nUnless a licence is specified above, all rights (including copyright and moral rights) in this document are retained by the authors and/or the copyright holders. The express permission of the copyright holder must be obtained for any use of this material other than for purposes permitted by law.  \n•Users may freely distribute the URL that is used to identify this publication.  \n•Users may download and/or print one copy of the publication from the University of Birmingham research portal for the purpose of private study or non-commercial research.  \n•User may use extracts from the document in line with the concept of ‘fair dealing’ under the Copyright, Designs and Patents Act 1988 (?)  \n•Users may not further distribute the material nor use it for the purposes of commercial gain.  \nWhere a licence is displayed above, please note the terms and conditions of the licence govern your use of this document.  \nWhen citing, please reference the published version.  \nTake down policy  \nWhile the University of Birmingham exercises care and attention in making items available there are rare occasions when an item has been uploaded in error or has been deemed to be commercially or otherwise sensitive.  \nIf you believe that this is the case for this document, [please contact UBIRA@lists.bham.ac.uk](please contact UBIRA@lists.bham.ac.uk) providing details and we will remove access to the work immediately and investigate.  \nDownload date: 03. Aug. 2026  \n[www. nature.com/scientificreports](www. nature.com/scientificreports)  \nOPEN  \nIdentifying COVID‑19 survivors living with post‑traumatic stress disorder through machine learning on Twitter  \nAnees Baqir1,9, Mubashir Ali2, Shaista Jaffar3, Hafiz Husnain Raza Sherazi4*, Mark Lee2, Ali Kashif Bashir5,6,7 & Maryam M. Al Dabel8  \nThe COVID‑19 pandemic has disrupted people’s lives and caused significant economic damage around the world, but its impact on people’s mental health has not been paid due attention by the research community. According to anecdotal data, the pandemic has raised serious concerns related to mental health among the masses. However, no systematic investigations have been conducted previously on mental health monitoring and, in particular, detection of post‑traumatic stress disorder (PTSD). The goal of this study is to use classical machine learning approaches to classify tweets into COVID‑PTSD positive or negative categories. To this end, we employed various Machine Learning (ML) classifiers, to segregate the psychotic difficulties with the user’s PTSD in the context ofCOVID‑19, including Random Forest Support Vector Machine, Naïve Bayes, and K‑Nearest Neighbor. ML models are trained and tested using various combinations of feature selection strategies to get the best possible combination. Based on our experimentation on real‑world dataset, we demonstrate our model’s effectiveness to perform classification with an accuracy of 83.29% using Support Vector Machine as classifier and unigram as a feature pattern.  \nThe COVID-19 virus, which rapidly spread across the world since late 2019, was first detected on December 31, 201","cbCaie93leJ4kYRY","https://ap.wps.com/l/cbCaie93leJ4kYRY","pdf",1349734,1,15,"English","en",105,"# Background and motivation\n# Methodology: machine learning tweet classification\n# Evaluation and results\n# COVID-19 context and mental-health impact\n# Data and broader research challenges","[{\"question\":\"What is the main goal of the study?\",\"answer\":\"To classify tweets as COVID-PTSD positive or negative using classical machine learning approaches.\"},{\"question\":\"How do the authors detect PTSD-related signals in Twitter data?\",\"answer\":\"They train and test multiple machine learning classifiers on real-world datasets, using feature selection strategies and patterns such as unigram features.\"},{\"question\":\"What classification performance does the study report?\",\"answer\":\"The study reports 83.29% accuracy when using Support Vector Machine with unigram features.\"}]","Identifying COVID-19 survivors living with post-traumatic stress disorder through machine learning on Twitter | 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