Deep Learning Architectures and Strategies for Early Detection of Self-harm and Depression Level Prediction

Abstract

[EN] This paper summarizes the contributions of the PRHLT- UPV team as a participant in the eRisk 2020 tasks on self-harm detection and prediction of depression levels from social media. Computational methods based on machine learning and natural language processing have a great potential to assist with early detection of mental disorders of social media users, based on their online activity.We use multi-dimensional representations of language, and compare various deep learning models' performance, exploring rarely approached avenues in previous research, including hierarchical deep learning architectures and pre-trained transformers and language models.The work of Paolo Rosso was in the framework of the research project PROMETEO/2019/121 (DeepPattern) by the Generalitat Valenciana.Uban, A.; Rosso, P. (2020). Deep Learning Architectures and Strategies for Early Detection of Self-harm and Depression Level Prediction. CEUR Workshop Proceedings. 2696:1-12. http://hdl.handle.net/10251/166536S112269