A framework for applying natural language processing in digital health interventions

BACKGROUND: Digital health interventions (DHIs) are poised to reduce target symptoms in a scalable, affordable, and empirically supported way. DHIs that involve coaching or clinical support often collect text data from 2 sources: (1) open correspondence between users and the trained practitioners supporting them through a messaging system and (2) text data recorded during the intervention by users, such as diary entries. Natural language processing (NLP) offers methods for analyzing text, augmenting the understanding of intervention effects, and informing therapeutic decision making. OBJECTIVE: This study aimed to present a technical framework that supports the automated analysis of both types of text data often present in DHIs. This framework generates text features and helps to build statistical models to predict target variables, including user engagement, symptom change, and therapeutic outcomes. METHODS: We first discussed various NLP techniques and demonstrated how they are implemented in the presented framework. We then applied the framework in a case study of the Healthy Body Image Program, a Web-based intervention trial for eating disorders (EDs). A total of 372 participants who screened positive for an ED received a DHI aimed at reducing ED psychopathology (including binge eating and purging behaviors) and improving body image. These users generated 37,228 intervention text snippets and exchanged 4285 user-coach messages, which were analyzed using the proposed model. RESULTS: We applied the framework to predict binge eating behavior, resulting in an area under the curve between 0.57 (when applied to new users) and 0.72 (when applied to new symptom reports of known users). In addition, initial evidence indicated that specific text features predicted the therapeutic outcome of reducing ED symptoms. CONCLUSIONS: The case study demonstrates the usefulness of a structured approach to text data analytics. NLP techniques improve the prediction of symptom changes in DHIs. We present a technical framework that can be easily applied in other clinical trials and clinical presentations and encourage other groups to apply the framework in similar contexts

Relación entre la Topología de Last.fm y el comportamiento de los usuarios en el medio social

Projecte final de carrera fet en col.laboració amb Faculty of Electrical Engineering, Mathematics and Computer Science. Delft University of TechnologyActualmente, la mayoría de las redes sociales en Internet son comunidades de personas que comparten actividades e intereses, o que están interesadas en explorar las actividades e intereses de otros usuarios. El estudio tanto de la topología de estas redes sociales como del comportamiento de los usuarios en la red es fundamental para poder mejorar las características funcionales de la red. La red social Last.fm nos da la oportunidad de obtener diversa información pública de los usuarios: amistades, preferencias musicales y etiquetado de música. El objetivo principal de este proyecto es determinar si existe alguna relación entre la actividad social de los usuarios (amigos) y el uso de las funciones específicas de la red social. En el siguiente documento, primero estudiamos la topología de la red en términos de distribución nodal y simetría de enlaces, y después relacionamos la distribución nodal con la actividad en la red. A lo largo del documento extraemos una serie de interesantes conclusiones. Usuarios que escuchan música no popular tienden a tener más amigos que los usuarios que escuchan música popular. La causa de esta tendencia se debe a que los usuarios con gustos no populares están obligados a compartir sus intereses con sus amigos y buscar nuevos grupos a través de los perfiles de otros usuarios. Por el contrario, los usuarios con gustos populares no tienen estas necesidades y consecuentemente tienen menos amigos en la red. También llegamos a la conclusión de que los usuarios que suelen etiquetar la música que escuchan (tagging), también suelen tener más amigos ya que están explotando más la red social en todos los sentidos y están ayudando a otros usuarios a encontrar la música con facilidad. Con el conocimiento de cómo los usuarios actúan en la red social podremos desarrollar nuevos algoritmos que mejoren las actuales características de las redes. Un algoritmo que mejorase el inicio del proceso de hacer amistades o publicidad personalizada dependiendo de los gustos musicales son dos ejemplos de posibles mejoras que se podrían desarrollar en un futuro

Understanding Socialization Efficacy and Loneliness of Baby Boomers through Facebook

Baby boomers are the largest age cohort in the United States, making up approximately 20% of the population. This cohort is faced with global problems that contribute to perceived loneliness and a lack of socialization. Additionally, baby boomers have an increased online presence on Facebook (FB), yet little is known about this age group and FB use. This research study addressed this issue with an examination of the relationships between overall perceived loneliness, socialization efficacy, and FB use. The theoretical framework that guided this study was Bandura\u27s social learning theory, which was used to examine the effects of social reinforcement. Participants, those born between 1946 and 1964, (n = 97) were asked to share 2 months of FB activity, including the number of FB friends, number of postings, types of postings, quotes included, status updates, articles reposted, and whether friends were tagged in their posts. The FB variables were correlated with perceived loneliness, as measured by the Revised UCLA Loneliness Scale, and socialization efficacy, as measured by the Social Skill Scale, through a stepwise multiple regression analysis. The findings did not yield any statistically significant relationships between the FB variables and loneliness or socialization efficacy among baby boomers. These findings imply that other factors not studied here are promoting the increase in baby boomer FB use. The social change implications include mental health clinicians having a deeper knowledge base of baby boomers\u27 FB use and an accurate portrayal of this cohort for increased treatment effectiveness, as baby boomers are portrayed as being lonely, isolated, and technologically challenged, which was not empirically supported in this study

Recommender Systems

The ongoing rapid expansion of the Internet greatly increases the necessity of effective recommender systems for filtering the abundant information. Extensive research for recommender systems is conducted by a broad range of communities including social and computer scientists, physicists, and interdisciplinary researchers. Despite substantial theoretical and practical achievements, unification and comparison of different approaches are lacking, which impedes further advances. In this article, we review recent developments in recommender systems and discuss the major challenges. We compare and evaluate available algorithms and examine their roles in the future developments. In addition to algorithms, physical aspects are described to illustrate macroscopic behavior of recommender systems. Potential impacts and future directions are discussed. We emphasize that recommendation has a great scientific depth and combines diverse research fields which makes it of interests for physicists as well as interdisciplinary researchers.Comment: 97 pages, 20 figures (To appear in Physics Reports

Learning Multimodal Latent Attributes

Abstract—The rapid development of social media sharing has created a huge demand for automatic media classification and annotation techniques. Attribute learning has emerged as a promising paradigm for bridging the semantic gap and addressing data sparsity via transferring attribute knowledge in object recognition and relatively simple action classification. In this paper, we address the task of attribute learning for understanding multimedia data with sparse and incomplete labels. In particular we focus on videos of social group activities, which are particularly challenging and topical examples of this task because of their multi-modal content and complex and unstructured nature relative to the density of annotations. To solve this problem, we (1) introduce a concept of semi-latent attribute space, expressing user-defined and latent attributes in a unified framework, and (2) propose a novel scalable probabilistic topic model for learning multi-modal semi-latent attributes, which dramatically reduces requirements for an exhaustive accurate attribute ontology and expensive annotation effort. We show that our framework is able to exploit latent attributes to outperform contemporary approaches for addressing a variety of realistic multimedia sparse data learning tasks including: multi-task learning, learning with label noise, N-shot transfer learning and importantly zero-shot learning