A text classification framework for simple and effective early depression detection over social media streams

• Autores: Burdisso, Sergio Gastón; Errecalde, Marcelo Luis; Montes y Gómez, Manuel
• Año de publicación: 2019
• Idioma: inglés
• Tipo de recurso: artículo
• Materia: EARLY DEPRESSION DETECTION; EARLY TEXT CLASSIFICATION; EXPLAINABILITY; INCREMENTAL CLASSIFICATION; INTERPRETABILITY; SS3
• Institución: Consejo Nacional de Investigaciones Científicas y Técnicas

With the rise of the Internet, there is a growing need to build intelligent systems that are capable of efficiently dealing with early risk detection (ERD) problems on social media, such as early depression detection, early rumor detection or identification of sexual predators. These systems, nowadays mostly based on machine learning techniques, must be able to deal with data streams since users provide their data over time. In addition, these systems must be able to decide when the processed data is sufficient to actually classify users. Moreover, since ERD tasks involve risky decisions by which people's lives could be affected, such systems must also be able to justify their decisions. However, most standard and state-of-the-art supervised machine learning models (such as SVM, MNB, Neural Networks, etc.) are not well suited to deal with this scenario. This is due to the fact that they either act as black boxes or do not support incremental classification/learning. In this paper we introduce SS3, a novel supervised learning model for text classification that naturally supports these aspects. SS3 was designed to be used as a general framework to deal with ERD problems. We evaluated our model on the CLEF's eRisk2017 pilot task on early depression detection. Most of the 30 contributions submitted to this competition used state-of-the-art methods. Experimental results show that our classifier was able to outperform these models and standard classifiers, despite being less computationally expensive and having the ability to explain its rationale.
Fil: Burdisso, Sergio Gastón. Universidad Nacional de San Luis; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Luis; Argentina
Fil: Errecalde, Marcelo Luis. Universidad Nacional de San Luis; Argentina
Fil: Montes y Gómez, Manuel. Instituto Nacional de Astrofísica, Óptica y Electrónica; México