A Review: PTSD in Pre-Existing Medical Condition on Social Media

TL;DR

This review explores how social media data can be used to understand and support individuals with PTSD and pre-existing medical conditions, highlighting NLP and ML techniques for early detection and intervention.

Contribution

It systematically analyzes literature from 2008 to 2024, demonstrating social media's potential for monitoring PTSD in chronic illness populations using NLP and ML methods.

Findings

Social media data provides insights into PTSD challenges among chronically ill individuals.

NLP and ML techniques achieve 74-90% accuracy in identifying PTSD cases.

Online support communities influence coping strategies and early intervention.

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.