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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\u2019s PTSD in the context of COVID-19, including Random Forest Support Vector Machine, Na\u00efve 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\u2019s effectiveness to perform classification with an accuracy of 83.29% using Support Vector Machine as classifier and unigram as a feature pattern.<\/jats:p>","DOI":"10.1038\/s41598-024-69687-8","type":"journal-article","created":{"date-parts":[[2024,8,14]],"date-time":"2024-08-14T16:02:36Z","timestamp":1723651356000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Identifying COVID-19 survivors living with post-traumatic stress disorder through machine learning on Twitter"],"prefix":"10.1038","volume":"14","author":[{"given":"Anees","family":"Baqir","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mubashir","family":"Ali","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shaista","family":"Jaffar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8152-4065","authenticated-orcid":false,"given":"Hafiz Husnain Raza","family":"Sherazi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mark","family":"Lee","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ali Kashif","family":"Bashir","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Maryam M.","family":"Al Dabel","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,8,14]]},"reference":[{"key":"69687_CR1","unstructured":"WHO. 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