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

arXiv: https://arxiv.org/abs/2601.08836 · HTML: https://mlsanity.com/paper/2601.08836/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 80 references that are in the map.
- First posted on arXiv 15 Dec 2025.

## Builds on (most cited first, 7 of 7)

- [Extracting Training Data from Large Language Models](https://mlsanity.com/paper/2012.07805/) (2020)
- [The Next Decade in AI: Four Steps Towards Robust Artificial Intelligence](https://mlsanity.com/paper/2002.06177/) (2020)
- [MentaLLaMA: Interpretable Mental Health Analysis on Social Media with Large Language Models](https://mlsanity.com/paper/2309.13567/) (2023)
- [The opportunities and risks of large language models in mental health](https://mlsanity.com/paper/2403.14814/) (2024)
- [Forecasting the onset and course of mental illness with Twitter data](https://mlsanity.com/paper/1608.07740/) (2016)
- [Multi-Task Learning for Mental Health using Social Media Text](https://mlsanity.com/paper/1712.03538/) (2017)
- [Detecting and Explaining Crisis](https://mlsanity.com/paper/1705.09585/) (2017)

## Abstract (arXiv)

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.