[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123870-en":3,"doc-seo-123870-105":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},123870,549758252649,"Ivy","https://ap-avatar.wpscdn.com/avatar/8000253669c5317157?_k=1778319167496531819",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 produced widespread concern over mental health, yet systematic monitoring for post-traumatic stress disorder (PTSD) has been limited. This study uses classical machine learning to classify tweets into COVID‑PTSD positive versus negative categories. Multiple ML classifiers, including Random Forest, Support Vector Machine, Naïve Bayes, and K‑Nearest Neighbor, are trained and evaluated with combinations of feature selection strategies. Results on real-world Twitter data show effective detection with 83.29% accuracy using Support Vector Machine with unigram features.","[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, 2019. World Health Organization (WHO) declared it a pandemic On March 11, 2020. As of March 13, 2023, the virus has infected people in 216 countries, with over 759.41 million confirmed cases and over 6.87 million confirmed deaths1. In response to the pandemic, educational facilities in 190 countries were closed, and many governments issued flight bans and stay-at-home orders, affecting people worldwide2.  \nPrior to the COVID-19 outbreak, an estimated 380 million people worldwide, of all ages, were affected by mental health issues. Previous studies have shown that mental health problems can lead to harmful outcomes such as suicide3,4. However, these studies face two major challenges. Firstly, many individuals with mental health problems are hesitant or ashamed to seek help5. Secondly, obtaining and analyzing a large sample size of diagnosed individuals can be difficult in psychological research.  \nNumerous studies have investigated the economic and social impacts of COVID-196,7. In addition, various investigations have revealed that the mental health of people around the world has been greatly affected by the COVID-19 outbreak. These studies have reported higher rates of depression, anxiety, PTSD, and stress symptoms during the pandemic than before8. While stay-at-home orders and social distancing measures have been effective in preventing the spread ofCOVID-19, as suggested by previous research, they can also have negative effects on individuals’ mental health9–11.  \nSocial media data can provide valuable insights into physical and mental health concerns. Often, social media users are unaware of changes in their own health12. Research has demonstrated that searching for information about certain health problems can reveal early-warning signs of hard-to-detect tumors13. Social media platforms  \n1Complex Human Behavior Laboratory, Fondazione Bruno Kessler, Trento, Italy. 2School of Computer Science, University of Birmingham, Birmingham, UK. 3National Drug and Treatment Center, Dublin, Ireland. 4School of Computing, Newcastle University, Newcastle Upon Tyne, UK. 5Department of Computing and Mathematics, Manchester Metropolitan University, Manchester, UK. 6Woxsen School of Business, Woxsen University, Hyderabad 502 345, India. 7Department of Computer Science and Mathematics, Lebanese American Univers","cbCaiqRBsGJx9oyy","https://ap.wps.com/l/cbCaiqRBsGJx9oyy","pdf",1317021,1,14,"English","en",105,"# Motivation\n## Depression and computational screening\n## PTSD underdiagnosis and care opportunities\n# Study Objective and Approach\n## Tweet classification into COVID‑PTSD positive/negative\n## Machine learning classifiers and feature selection\n# Background and Context\n## COVID-19 spread and global impact\n## Mental health burden and research challenges\n## Social media as a monitoring signal","[{\"question\":\"What problem does the study address about COVID-19 and mental health?\",\"answer\":\"It targets the lack of systematic investigations for monitoring post-traumatic stress disorder (PTSD) during the COVID-19 pandemic.\"},{\"question\":\"How does the study detect COVID-PTSD from Twitter data?\",\"answer\":\"It classifies tweets into COVID‑PTSD positive or negative categories using classical machine learning models trained on real-world datasets.\"},{\"question\":\"Which machine learning setup achieved the best reported performance?\",\"answer\":\"Support Vector Machine with unigram features delivered 83.29% accuracy in the authors’ experiments.\"}]","Identifying COVID‑19 survivors living with post‑traumatic stress disorder through machine learning on Twitter | 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