Abstract: Post-Traumatic Stress Disorder (PTSD) is a multifaceted mental health condition, particularly challenging for individuals with pre-existing medical conditions. This review critically examines the intersection of PTSD and chronic illnesses as expressed on social media platforms. By systematically analyzing literature from 2008 to 2024, the study explores how PTSD manifests and is managed in individuals with chronic conditions such as cancer, heart disease, and autoimmune disorders, with a focus on online expressions on platforms like X (formally known as Twitter) and Facebook. Findings demonstrate that social media data offers valuable insights into the unique challenges faced by individuals with both PTSD and chronic illnesses. Specifically, natural language processing (NLP) and machine learning (ML) techniques can identify potential PTSD cases among these populations, achieving accuracy rates between 74% and 90%. Furthermore, the role of online support communities in shaping coping strategies and facilitating early interventions is highlighted. This review underscores the necessity of incorporating considerations of pre-existing medical conditions in PTSD research and treatment, emphasizing social media's potential as a monitoring and support tool for vulnerable groups. Future research directions and clinical implications are also discussed, with an emphasis on developing targeted interventions.

Zaber Al Hassan Ayon, Nur Hafieza Ismail and Nur Shazwani Kamarudin, “A Review: PTSD in Pre-Existing Medical Condition on Social Media” International Journal of Advanced Computer Science and Applications(IJACSA), 15(11), 2024. http://dx.doi.org/10.14569/IJACSA.2024.0151194

@article{Ayon2024,
title = {A Review: PTSD in Pre-Existing Medical Condition on Social Media},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.0151194},
url = {http://dx.doi.org/10.14569/IJACSA.2024.0151194},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {11},
author = {Zaber Al Hassan Ayon and Nur Hafieza Ismail and Nur Shazwani Kamarudin}
}