Year: 2019
Type: conference-paper
Abstract: Mental health poses a significant challenge for an individual's well-being.Text analysis of rich resources, like social media, can contribute to deeper understanding of illnesses and provide means for their early detection.We tackle a challenge of detecting social media users' mental status through deep learningbased models, moving away from traditional approaches to the task.In a binary classification task on predicting if a user suffers from one of nine different disorders, a hierarchical attention network outperforms previously set benchmarks for four of the disorders.Furthermore, we explore the limitations of our model and analyze phrases relevant for classification by inspecting the model's word-level attention weights.
DOI: 10.18653/v1/d19-5542
Authors: Ivan Sekulic, Michael Strube
Institution: Heidelberg Institute for Theoretical Studies
Language: English
Topic: Mental Health via Writing
Subfield: Social Psychology
Field: Psychology
Domain: Social Sciences
Open Access status: hybrid
OpenAlex ID: W2984239773