Detecting Mental Disorders in Social Media using a Multichannel Representation
Millions of people around the world are affected by one or more mental disorders that in- terfere with their thinking and behavior. Timely detection of these issues is challenging but crucial since it could open the possibility to offer help to people before the illness gets worse. One alternative t...
| Autor: | |
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| Tipo de recurso: | tesis doctoral |
| Estado: | Versión aceptada para publicación |
| Fecha de publicación: | 2022 |
| País: | México |
| Institución: | Instituto Nacional de Astrofísica, Óptica y Electrónica |
| Repositorio: | Repositorio Institucional del INAOE |
| Idioma: | inglés |
| OAI Identifier: | oai:inaoe.repositorioinstitucional.mx:1009/2340 |
| Acceso en línea: | http://inaoe.repositorioinstitucional.mx/jspui/handle/1009/2340 |
| Access Level: | acceso abierto |
| Palabra clave: | info:eu-repo/classification/Inspec/Desórdenes mentales info:eu-repo/classification/Inspec/Representación multicanal info:eu-repo/classification/Inspec/Aprendizaje profundo info:eu-repo/classification/cti/1 info:eu-repo/classification/cti/12 info:eu-repo/classification/cti/1203 info:eu-repo/classification/cti/120312 |
| Sumario: | Millions of people around the world are affected by one or more mental disorders that in- terfere with their thinking and behavior. Timely detection of these issues is challenging but crucial since it could open the possibility to offer help to people before the illness gets worse. One alternative to accomplish this is to monitor how people express themselves, that is for example what and how they write, or even a step further, what emotions they express in their social media communications. Over the last few years, studies related to the detection of mental disorders in social media have been increasing. The latter because the awareness created by health campaigns that emphasize the commonness of these disorders among all of us, that also has motivated the creation of new datasets, many of them extracted from social media platforms. In this study, we aim to contribute with the analysis of three major mental disorders that are hitting the world: Anorexia, Self-harm, and Depression. To this end, we propose a novel model that, first, extracts three different views, or information channels, from the posts shared by users: thematic interests, writing style, and emotions. Then, it fusions the information from each channel by using a gated multi-modal unit a module that learns the relations between channels. We evaluate the feasibility of our approach in the aforementioned tasks, first by comparing its output against traditional and modern strategies, and later against the best contestants in the eRisk evaluation forum, a workshop that explores issues related to the evaluation of methodologies and practical applications of topics related to health and safety for early risk detection on the internet. |
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