Name
Abstract | ||
|---|---|---|
Post Traumatic Stress Disorder (PTSD) is a public health problem afflicting millions of people each year. It is especially prominent among military veterans. Understanding the language, attitudes, and topics associated with PTSD presents an important and challenging problem. Based on their expertise, mental health professionals have constructed a formal definition of PTSD. However, even the most assiduous mental health professionals can care for only a small fraction of those suffering from PTSD, limiting their perspective of the disorder. As social networking sites have grown in acceptance, users have begun to express personal thoughts and feelings, such as those related to PTSD. This wealth of content can be viewed as an enormous collective description of PTSD and its related issues. We automatically extract informal latent topics from thousands of social media posts in which users describe their experience with PTSD and compare these topics to the formal description generated by mental health professionals. We then explore the pattern and associations of these topics. Our informal topic discovery evaluation reveals that we can successfully identify meaningful topics in PTSD social media related data. When comparing our topics to the criteria included in the Diagnostic and Statistical Manual of Mental Disorders (DSM), we found that we were able to automatically reproduce many of the criteria. We also discovered new topics which were not mentioned in the DSM, but were prevalent across the collaborative narrative of thousands of user's experience with PTSD. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/ICDMW.2017.65 | 2017 IEEE International Conference on Data Mining Workshops (ICDMW) |
Keywords | Field | DocType |
Post Traumatic Stress Disorder (PTSD),Topic Modeling,Word Embeddings,Association Rules | Public health,Social network,Social media,Traumatic stress,Computer science,Narrative,Artificial intelligence,Mental health,Feeling,Applied psychology,Semantics,Machine learning | Conference |
ISSN | ISBN | Citations |
2375-9232 | 978-1-5386-3801-9 | 0 |
PageRank | References | Authors |
0.34 | 20 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Reilly Grant | 1 | 0 | 0.34 |
| David Kucher | 2 | 0 | 0.68 |
| Ana M. Leon | 3 | 0 | 0.34 |
| Jonathan Gemmell | 4 | 415 | 20.73 |
| Daniela Stan Raicu | 5 | 469 | 46.22 |