About: Understanding Depression from Psycholinguistic Patterns in Social Media Texts

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An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Understanding Depression from Psycholinguistic Patterns in Social Media Texts
Creator	Antunes, Rui Matos, Sérgio Oliveira, Jose Trifan, Alina
Source	PMC
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.
has issue date	2020-03-24(xsd:dateTime)
bibo:doi	10.1007/978-3-030-45442-5_50
has license	no-cc
sha1sum (hex)	bd6d075b64c234ae611aad753e84db83556d05f7
schema:url	https://doi.org/10.1007/978-3-030-45442-5_50
resource representing a document's title	Understanding Depression from Psycholinguistic Patterns in Social Media Texts
has PubMed Central identifier	PMC7148066
schema:publication	Advances in Information Retrieval
resource representing a document's body	covid:bd6d075b64c234ae611aad753e84db83556d05f7#body_text
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