Back to Search
Start Over
Understanding Depression from Psycholinguistic Patterns in Social Media Texts
- Source :
- Lecture Notes in Computer Science ISBN: 9783030454418, ECIR (2)
- Publication Year :
- 2020
- Publisher :
- Springer International Publishing, 2020.
-
Abstract
- The World Health Organization reports that half of all mental illnesses begin by the age of 14. Most of these cases go undetected and untreated. The expanding use of social media has the potential to leverage the early identification of mental health diseases. As data gathered via social media are already digital, they have the ability to power up faster automatic analysis. In this article we evaluate the impact that psycholinguistic patterns can have on a standard machine learning approach for classifying depressed users based on their writings in an online public forum. We combine psycholinguistic features in a rule-based estimator and we evaluate their impact on this classification problem, along with three other standard classifiers. Our results on the Reddit Self-reported Depression Diagnosis dataset outperform some previously reported works on the same dataset. They stand for the importance of extracting psychologically motivated features when processing social media texts with the purpose of studying mental health.
- Subjects :
- 03 medical and health sciences
Public forum
0302 clinical medicine
Computer science
0202 electrical engineering, electronic engineering, information engineering
Leverage (statistics)
020201 artificial intelligence & image processing
Social media
02 engineering and technology
Mental health
World health
030227 psychiatry
Cognitive psychology
Subjects
Details
- ISBN :
- 978-3-030-45441-8
- ISBNs :
- 9783030454418
- Database :
- OpenAIRE
- Journal :
- Lecture Notes in Computer Science ISBN: 9783030454418, ECIR (2)
- Accession number :
- edsair.doi...........4cb8cfeb68148eddaf53baccd4786b7b