Taming Wild High Dimensional Text Data with a Fuzzy Lash

The bag of words (BOW) represents a corpus in a matrix whose elements are the frequency of words. However, each row in the matrix is a very high-dimensional sparse vector. Dimension reduction (DR) is a popular method to address sparsity and high-dimensionality issues. Among different strategies to develop DR method, Unsupervised Feature Transformation (UFT) is a popular strategy to map all words on a new basis to represent BOW. The recent increase of text data and its challenges imply that DR area still needs new perspectives. Although a wide range of methods based on the UFT strategy has been developed, the fuzzy approach has not been considered for DR based on this strategy. This research investigates the application of fuzzy clustering as a DR method based on the UFT strategy to collapse BOW matrix to provide a lower-dimensional representation of documents instead of the words in a corpus. The quantitative evaluation shows that fuzzy clustering produces superior performance and features to Principal Components Analysis (PCA) and Singular Value Decomposition (SVD), two popular DR methods based on the UFT strategy

Computational Content Analysis of Negative Tweets for Obesity, Diet, Diabetes, and Exercise

Social media based digital epidemiology has the potential to support faster response and deeper understanding of public health related threats. This study proposes a new framework to analyze unstructured health related textual data via Twitter users' post (tweets) to characterize the negative health sentiments and non-health related concerns in relations to the corpus of negative sentiments, regarding Diet Diabetes Exercise, and Obesity (DDEO). Through the collection of 6 million Tweets for one month, this study identified the prominent topics of users as it relates to the negative sentiments. Our proposed framework uses two text mining methods, sentiment analysis and topic modeling, to discover negative topics. The negative sentiments of Twitter users support the literature narratives and the many morbidity issues that are associated with DDEO and the linkage between obesity and diabetes. The framework offers a potential method to understand the publics' opinions and sentiments regarding DDEO. More importantly, this research provides new opportunities for computational social scientists, medical experts, and public health professionals to collectively address DDEO-related issues.Comment: The 2017 Annual Meeting of the Association for Information Science and Technology (ASIST

Characterizing Transgender Health Issues in Twitter

Although there are millions of transgender people in the world, a lack of information exists about their health issues. This issue has consequences for the medical field, which only has a nascent understanding of how to identify and meet this population's health-related needs. Social media sites like Twitter provide new opportunities for transgender people to overcome these barriers by sharing their personal health experiences. Our research employs a computational framework to collect tweets from self-identified transgender users, detect those that are health-related, and identify their information needs. This framework is significant because it provides a macro-scale perspective on an issue that lacks investigation at national or demographic levels. Our findings identified 54 distinct health-related topics that we grouped into 7 broader categories. Further, we found both linguistic and topical differences in the health-related information shared by transgender men (TM) as com-pared to transgender women (TW). These findings can help inform medical and policy-based strategies for health interventions within transgender communities. Also, our proposed approach can inform the development of computational strategies to identify the health-related information needs of other marginalized populations

SOCIAL MEDIA ANALYSIS FOR ORGANIZATIONS: US NORTHEASTERN PUBLIC AND STATE LIBRARIES CASE STUDY

Social networking sites such as Twitter have provided a great opportunity for organizations such as public libraries to disseminate information for public relations purposes. However, there is a need to analyze vast amounts of social media data. This study presents a computational approach to explore the content of tweets posted by nine public libraries in the northeastern United States of America. In December 2017, this study extracted more than 19,000 tweets from the Twitter accounts of seven state libraries and two urban public libraries. Computational methods were applied to collect the tweets and discover meaningful themes. This paper shows how the libraries have used Twitter to represent their services and provides a starting point for different organizations to evaluate the themes of their public tweets

Los roles del bienestar espiritual y la tolerancia a la incertidumbre en la predicción de la felicidad en los ancianos

Background: According to spirituality well-being, ambiguity intolerance, and happiness conceptualizations, this study was purposed to investigate the influences of spiritual well-being and uncertainty tolerance on happiness with regards to the moderating roles of sex in the elderly. Method: Participants included 120 elders from Shiraz City, Fars province, Iran. A demographic questionnaire, the Spiritual Well-Being Inventory (SWBI), the Multiple Stimulus Types Ambiguity Tolerance Scale-II (MSTAT–II), and the Oxford Happiness Questionnaire (OHI) were used for data collection. Results: Findings showed that spirituality well-being and uncertainty intolerance explain 60% of happiness variation in the elderly. But results rejected the role of sex on the prediction of happiness in the present study. Conclusion: This study demonstrates the predictive roles of spiritual well-being and ambiguity tolerance on happiness in the field of gerontology.Antecedentes: De acuerdo con las conceptualizaciones del bienestar espiritual, la intolerancia a la ambigüedad y la felicidad, este estudio se propuso investigar las influencias del bienestar espiritual y la tolerancia a la incertidumbre sobre la felicidad con respecto a los roles moderadores del sexo en los ancianos. Método: Participaron 120 ancianos de la ciudad de Shiraz, provincia de Fars, Irán. Para la recopilación de datos se utilizaron un cuestionario demográfico, el Inventario de Bienestar Espiritual (SWBI), la Escala II de Tolerancia a la Ambigüedad de Tipos de Estímulos Múltiples (MSTAT-II) y el Cuestionario de Felicidad de Oxford (OHI). Resultados: Los resultados mostraron que la espiritualidad, el bienestar y la intolerancia a la incertidumbre explican el 60% de la variación de la felicidad en los ancianos. Pero los resultados rechazaron el papel del sexo en la predicción de la felicidad en el presente estudio. Conclusión: Este estudio demuestra los roles predictivos del bienestar espiritual y la tolerancia a la ambigüedad sobre la felicidad en el campo de la gerontología

CHARACTERIZING DISEASES AND DISORDERS IN GAY USERS’ TWEETS

A lack of information exists about the health issues of lesbian, gay, bisexual, transgender, and queer (LGBTQ) people who are often excluded from national demographic assessments, health studies, and clinical trials. As a result, medical experts and researchers lack holistic understanding of the health disparities facing these populations. Fortunately, publicly available social media data such as Twitter data can be utilized to support the decisions of public health policy makers and managers with respect to LGBTQ people. This research employs a computational approach to collect tweets from gay users on healthrelated topics and model these topics. To determine the nature of health-related information shared by men who have sex with men on Twitter, we collected thousands of tweets from 177 active users. We sampled these tweets using a framework that can be applied to other LGBTQ sub-populations in future research. We found 11 diseases in 7 categories based on ICD 10 that are in line with the published studies and official reports