Towards Measuring the Severity of Depression in Social Media via Text Classification

Psychologists have used tests or carefully designed survey questions, such as Beck’s Depression Inventory (BDI), to identify the presence of depression and to assess its severity level. On the other hand, methods for automatic depression detection have gained increasing interest since all the information available in social media, such as Twitter and Facebook, enables novel measurement based on language use. These methods learn to characterize depression through natural language use and have shown that, in fact, language usage can provide strong evidence in detecting depressive people. However, not much attention has been paid to measuring finer grain relationships between both aspects, such as how is connected the language usage with the severity level of depression. The present study is a first step towards that direction. First, we train a binary text classifier to detect “depressed” users and then we use its confidence values to estimate the user’s clinical depression level. In order to do that, our system has to fill the standard BDI depression questionnaire on users’ behalf, based only on the text of users’ postings. Our proposal, publicly tested in the eRisk 2019 T3 task, obtained promising results. This offers very interesting evidence of the potential of our method to estimate the level of depression directly form user’s posts in social media.XVI Workshop Bases de Datos y Minería de Datos.Red de Universidades con Carreras en Informátic

Node-weighted Graph Convolutional Network for Depression Detection in Transcribed Clinical Interviews

We propose a simple approach for weighting self-connecting edges in a Graph Convolutional Network (GCN) and show its impact on depression detection from transcribed clinical interviews. To this end, we use a GCN for modeling non-consecutive and long-distance semantics to classify the transcriptions into depressed or control subjects. The proposed method aims to mitigate the limiting assumptions of locality and the equal importance of self-connections vs. edges to neighboring nodes in GCNs, while preserving attractive features such as low computational cost, data agnostic, and interpretability capabilities. We perform an exhaustive evaluation in two benchmark datasets. Results show that our approach consistently outperforms the vanilla GCN model as well as previously reported results, achieving an F1=0.84 on both datasets. Finally, a qualitative analysis illustrates the interpretability capabilities of the proposed approach and its alignment with previous findings in psychology.Comment: Paper Accepted to Interspeech 202

Towards Measuring the Severity of Depression in Social Media via Text Classification

Psychologists have used tests or carefully designed survey questions, such as Beck’s Depression Inventory (BDI), to identify the presence of depression and to assess its severity level. On the other hand, methods for automatic depression detection have gained increasing interest since all the information available in social media, such as Twitter and Facebook, enables novel measurement based on language use. These methods learn to characterize depression through natural language use and have shown that, in fact, language usage can provide strong evidence in detecting depressive people. However, not much attention has been paid to measuring finer grain relationships between both aspects, such as how is connected the language usage with the severity level of depression. The present study is a first step towards that direction. First, we train a binary text classifier to detect “depressed” users and then we use its confidence values to estimate the user’s clinical depression level. In order to do that, our system has to fill the standard BDI depression questionnaire on users’ behalf, based only on the text of users’ postings. Our proposal, publicly tested in the eRisk 2019 T3 task, obtained promising results. This offers very interesting evidence of the potential of our method to estimate the level of depression directly form user’s posts in social media.XVI Workshop Bases de Datos y Minería de Datos.Red de Universidades con Carreras en Informátic

Towards Measuring the Severity of Depression in Social Media via Text Classification

Psychologists have used tests or carefully designed survey questions, such as Beck’s Depression Inventory (BDI), to identify the presence of depression and to assess its severity level. On the other hand, methods for automatic depression detection have gained increasing interest since all the information available in social media, such as Twitter and Facebook, enables novel measurement based on language use. These methods learn to characterize depression through natural language use and have shown that, in fact, language usage can provide strong evidence in detecting depressive people. However, not much attention has been paid to measuring finer grain relationships between both aspects, such as how is connected the language usage with the severity level of depression. The present study is a first step towards that direction. First, we train a binary text classifier to detect “depressed” users and then we use its confidence values to estimate the user’s clinical depression level. In order to do that, our system has to fill the standard BDI depression questionnaire on users’ behalf, based only on the text of users’ postings. Our proposal, publicly tested in the eRisk 2019 T3 task, obtained promising results. This offers very interesting evidence of the potential of our method to estimate the level of depression directly form user’s posts in social media.XVI Workshop Bases de Datos y Minería de Datos.Red de Universidades con Carreras en Informátic

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

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; ArgentinaFil: Errecalde, Marcelo Luis. Universidad Nacional de San Luis; ArgentinaFil: Montes y Gómez, Manuel. Instituto Nacional de Astrofísica, Óptica y Electrónica; Méxic

T-World: un entorno gráfico, flexible y portable para la enseñanza e investigación de agentes inteligentes

Los entornos controlados pueden ser muy útiles en la investigación y la enseñanza de la Inteligencia Artificial, en general, y los Agentes Inteligentes en particular. Un entorno clásico es Tileworld, donde es posible ajustar diferentes controles del funcionamiento del ambiente de manera tal que se pueda evaluar el desempeño de las distintas arquitecturas y estrategias de control de los agentes. Más allá del atractivo que ofrece Tileworld como banco de pruebas, poco interés se ha dedicado a proveer implementaciones que aprovechen todas las características y posibilidades que ofrece el mismo para la investigación y docencia en el área de Agentes Inteligentes. En este contexto, este trabajo propone y describe a T-World, un entorno gráfico, flexible y portable para la ensenañanza e investigación de agentes inteligentes. T-World ofrece un entorno de trabajo visualmente atractivo e independiente de la plataforma que permite combinar distintas con figuraciones de ambientes y facilita la implementación e interacción con distintos tipos de agentes.III Workshop de Innovación en Educación en Informática (WIEI)Red de Universidades con Carreras de Informática (RedUNCI

Integración de agentes inteligentes heterogéneos en el entorno T-World

A fin de facilitar la tarea de crear y experimentar con agentes computacionales inteligentes se ha creado una plataforma que funciona como campo de experimentación portable. Cuenta con una simulación gráfica atractiva y numerosas cualidades que la hacen ideal para docencia. Uno de los desafíos más importantes es manejar la heterogeneidad de los agentes, lo que se consigue mediante un programa intermediario.XVI Workshop Agentes y Sistemas Inteligentes (WASI)Red de Universidades con Carreras en Informática (RedUNCI

T-World: un entorno gráfico, flexible y portable para la enseñanza e investigación de agentes inteligentes

Los entornos controlados pueden ser muy útiles en la investigación y la enseñanza de la Inteligencia Artificial, en general, y los Agentes Inteligentes en particular. Un entorno clásico es Tileworld, donde es posible ajustar diferentes controles del funcionamiento del ambiente de manera tal que se pueda evaluar el desempeño de las distintas arquitecturas y estrategias de control de los agentes. Más allá del atractivo que ofrece Tileworld como banco de pruebas, poco interés se ha dedicado a proveer implementaciones que aprovechen todas las características y posibilidades que ofrece el mismo para la investigación y docencia en el área de Agentes Inteligentes. En este contexto, este trabajo propone y describe a T-World, un entorno gráfico, flexible y portable para la ensenañanza e investigación de agentes inteligentes. T-World ofrece un entorno de trabajo visualmente atractivo e independiente de la plataforma que permite combinar distintas con figuraciones de ambientes y facilita la implementación e interacción con distintos tipos de agentes.III Workshop de Innovación en Educación en Informática (WIEI)Red de Universidades con Carreras de Informática (RedUNCI