Reconocimiento de depresión en redes sociales basado en la detección de síntomas

Depression is a common mental disorder that affects millions of people around the world. Recently, several methods have been proposed that detect people suffering from depression by analyzing their language patterns in social media. These methods show competitive results, but most of them are opaque and lack of explainability. Motivated by these problems, and inspired by the questionnaires used by health professionals for its diagnosis, in this paper we propose an approach for the detection of depression based on the identification and accumulation of evidence of symptoms through the users’ posts. Results in a benchmark collection are encouraging, as they show a competitive performance with respect to state-of-the-art methods. Furthermore, taking advantage of the approach’s properties, we outline what could be a support tool for healthcare professionals for analyzing and monitoring depression behaviors in social networks.La depresión es un trastorno mental que afecta a millones de personas en todo el mundo. Recientemente, se han propuesto varios métodos que detectan personas que sufren depresión analizando sus patrones de lenguaje en las redes sociales. Estos métodos han mostrado resultados competitivos, sin embargo la mayoría son opacos y carecen de explicabilidad. Motivados por estos problemas, e inspirados en los cuestionarios utilizados por los profesionales de la salud para su diagnóstico, en este trabajo proponemos un método para la detección de depresión basado en la identificación y acumulación de evidencia de síntomas a través de las publicaciones de los usuarios. Los resultados obtenidos en una colección de referencia son prometedores, ya que muestran un desempeño competitivo con respecto a los mejores métodos actuales. Además, aprovechando las propiedades del método, describimos lo que podría ser una herramienta de apoyo para que los profesionales de la salud analicen y monitoreen las conductas depresivas en las redes sociales

Prédiction de la détérioration du comportement à l’aide de l’apprentissage automatique

Les plateformes de médias sociaux rassemblent des individus pour interagir de manière amicale et civilisée tout en ayant des convictions et des croyances diversifiées. Certaines personnes adoptent des comportements répréhensibles qui nuisent à la sérénité et affectent négativement l’équanimité des autres utilisateurs. Certains cas de mauvaise conduite peuvent initialement avoir de petits effets statistiques, mais leur accumulation persistante pourrait entraîner des conséquences majeures et dévastatrices. L’accumulation persistante des mauvais comportements peut être un prédicteur valide des facteurs de risque de détérioration du comportement. Le problème de la détérioration du comportement n’a pas été largement étudié dans le contexte des médias sociaux. La détection précoce de la détérioration du comportement peut être d’une importance cruciale pour éviter que le mauvais comportement des individus ne s’aggrave. Cette thèse aborde le problème de la détérioration du comportement dans le contexte des médias sociaux. Nous proposons de nouvelles méthodes basées sur l’apprentissage automatique qui (1) explorent les séquences comportementales et leurs motifs temporels pour faciliter la compréhension des comportements manifestés par les individus et (2) prédisent la détérioration du comportement à partir de combinaisons consécutives de motifs séquentiels correspondant à des comportements inappropriés. Nous menons des expériences approfondies à l’aide d’ensembles de données du monde réel et démontrons la capacité de nos modèles à prédire la détérioration du comportement avec un haut degré de précision, c’est-à-dire des scores F-1 supérieurs à 0,8. En outre, nous examinons la trajectoire de détérioration du comportement afin de découvrir les états émotionnels que les individus présentent progressivement et d’évaluer si ces états émotionnels conduisent à la détérioration du comportement au fil du temps. Nos résultats suggèrent que la colère pourrait être un état émotionnel potentiel qui pourrait contribuer substantiellement à la détérioration du comportement

Predictive Analysis on Twitter: Techniques and Applications

Predictive analysis of social media data has attracted considerable attention from the research community as well as the business world because of the essential and actionable information it can provide. Over the years, extensive experimentation and analysis for insights have been carried out using Twitter data in various domains such as healthcare, public health, politics, social sciences, and demographics. In this chapter, we discuss techniques, approaches and state-of-the-art applications of predictive analysis of Twitter data. Specifically, we present fine-grained analysis involving aspects such as sentiment, emotion, and the use of domain knowledge in the coarse-grained analysis of Twitter data for making decisions and taking actions, and relate a few success stories

Computational and Causal Approaches on Social Media and Multimodal Sensing Data: Examining Wellbeing in Situated Contexts

A core aspect of our lives is often embedded in the communities we are situated in. The interconnectedness of our interactions and experiences intertwines our situated context with our wellbeing. A better understanding of wellbeing will help us devise proactive and tailored support strategies. However, existing methodologies to assess wellbeing suffer from limitations of scale and timeliness. These limitations are surmountable by social and ubiquitous technologies. Given its ubiquity and wide use, social media can be considered a “passive sensor” that can act as a complementary source of unobtrusive, real-time, and naturalistic data to infer wellbeing. This dissertation leverages social media in concert with multimodal sensing data, which facilitate analyzing dense and longitudinal behavior at scale. This work adopts machine learning, natural language, and causal inference analysis to infer wellbeing of individuals and collectives, particularly in situated communities, such as college campuses and workplaces. Before incorporating sensing modalities in practice, we need to account for confounds. One such confound that might impact behavior change is the phenomenon of “observer effect” --- that individuals may deviate from their typical or otherwise normal behavior because of the awareness of being “monitored”. I study this problem by leveraging the potential of longitudinal and historical behavioral data through social media. Focused on a multimodal sensing study, I conduct a causal study to measure observer effect in social media behavior, and explain the observations through existing theory in psychology and social science. The findings provide recommendations to correcting biases due to observer effect in social media sensing for human behavior and wellbeing. The novelties and contributions of this dissertation are four-fold. First, I use social media data that uniquely captures the behavior of situated communities. Second, I adopt theory-driven computational and causal methods to make conclusive research claims on wellbeing dynamics. Third, I address major challenges with methods to combine social media with multimodal sensing data for a comprehensive understanding of human behavior. Fourth, I draw interpretations and explanations of online-data-driven offline inferences. This dissertation situates the findings in an interdisciplinary context, including psychology and social science, and bears implications from theoretical, practical, design, methodological, and ethical perspectives catering to various stakeholders, including researchers, practitioners, and policymakers.Ph.D

Learning Disabilities

Learning disabilities are a heterogeneous group of disorders characterized by failure to acquire, retrieve, and use information competently. These disorders have a multifactorial aetiology and are most common and severe in children, especially when comorbid with other chronic health conditions. This book provides current and comprehensive information about learning disorders, including information on neurobiology, assessment, clinical features, and treatment. Chapters cover such topics as historical research and hypotheses of learning disorders, neuropsychological assessment and counselling, characteristics of specific disorders such as autism and ADHD, evidence-based treatment strategies and assistive technologies, and much more

Models and analysis of vocal emissions for biomedical applications

This book of Proceedings collects the papers presented at the 3rd International Workshop on Models and Analysis of Vocal Emissions for Biomedical Applications, MAVEBA 2003, held 10-12 December 2003, Firenze, Italy. The workshop is organised every two years, and aims to stimulate contacts between specialists active in research and industrial developments, in the area of voice analysis for biomedical applications. The scope of the Workshop includes all aspects of voice modelling and analysis, ranging from fundamental research to all kinds of biomedical applications and related established and advanced technologies

CLINICAL AND SOCIAL PATHWAYS TO CARE: A COMPUTATIONAL EXAMINATION OF SOCIAL MEDIA FOR MENTAL HEALTH CARE

In the last decade, powered by connectivity to large social networks and advances in collecting and analyzing digital traces of individuals from social media platforms, researchers have gleaned rich insights into individuals’ and populations’ mental health states and experiences, including their moods, emotions, social interactions, language, and communication patterns. Using these inferences, researchers have been able to study support-seeking behaviors, distinguishing patterns, risk markers, and diagnosis states for mental illnesses from social media data, promising a fundamental change in mental health care. What we need next in this line of work is for data and algorithms based on social media to be contextualized in people’s pathways to mental health care. However, there are several challenges and unanswered questions that present hurdles. First, gaps exist in the psychometric validity of social media based measurements of behaviors and the utility of these inferences in predicting clinical outcomes in patient populations. Second, if social media can act as an intervention platform, outside of discrete events, a holistic understanding of its role in people’s lives along the course of a mental illness is crucial. Lastly, several questions remain around the ethical implications of research practices in engaging with a vulnerable population subject to this research. This thesis charts out empirical and critical understandings and develops novel computational techniques to ethically and holistically examine how social media can be employed to support mental health care. Focusing on schizophrenia, one of the most debilitating and stigmatizing of mental illnesses, this thesis contributes a deeper understanding on pathways to care via social media along three themes: 1) prediction of clinical mental health states from social media data to support clinical interventions, 2) understanding online self-disclosure and social support as pathways to social care, and 3) the intersection of social and clinical pathways to care along the course of mental illness. In doing so, this work combines theories from social psychology, computer-mediated communication, and clinical literature with machine learning, statistical modeling, and natural language analysis methods applied on large-scale behavioral data from social media platforms. Together, this work contributes novel methodologies and human-centered algorithmic design frameworks to understand the efficacy of social media as a mental health intervention platform, informing clinicians, researchers, and designers who engage in developing and deploying interventions for mental health and well-being.Ph.D

Multimodal Depression Detection: An Investigation of Features and Fusion Techniques for Automated Systems

Depression is a serious illness that affects a large portion of the world’s population. Given the large effect it has on society, it is evident that depression is a serious health issue. This thesis evaluates, at length, how technology may aid in assessing depression. We present an in-depth investigation of features and fusion techniques for depression detection systems. We also present OpenMM: a novel tool for multimodal feature extraction. Lastly, we present novel techniques for multimodal fusion. The contributions of this work add considerably to our knowledge of depression detection systems and have the potential to improve future systems by incorporating that knowledge into their design