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

The 'who' and 'what' of #diabetes on Twitter

Social media are being increasingly used for health promotion, yet the landscape of users, messages and interactions in such fora is poorly understood. Studies of social media and diabetes have focused mostly on patients, or public agencies addressing it, but have not looked broadly at all the participants or the diversity of content they contribute. We study Twitter conversations about diabetes through the systematic analysis of 2.5 million tweets collected over 8 months and the interactions between their authors. We address three questions: (1) what themes arise in these tweets?, (2) who are the most influential users?, (3) which type of users contribute to which themes? We answer these questions using a mixed-methods approach, integrating techniques from anthropology, network science and information retrieval such as thematic coding, temporal network analysis, and community and topic detection. Diabetes-related tweets fall within broad thematic groups: health information, news, social interaction, and commercial. At the same time, humorous messages and references to popular culture appear consistently, more than any other type of tweet. We classify authors according to their temporal 'hub' and 'authority' scores. Whereas the hub landscape is diffuse and fluid over time, top authorities are highly persistent across time and comprise bloggers, advocacy groups and NGOs related to diabetes, as well as for-profit entities without specific diabetes expertise. Top authorities fall into seven interest communities as derived from their Twitter follower network. Our findings have implications for public health professionals and policy makers who seek to use social media as an engagement tool and to inform policy design.Comment: 25 pages, 11 figures, 7 tables. Supplemental spreadsheet available from http://journals.sagepub.com/doi/suppl/10.1177/2055207616688841, Digital Health, Vol 3, 201

360 Quantified Self

Wearable devices with a wide range of sensors have contributed to the rise of the Quantified Self movement, where individuals log everything ranging from the number of steps they have taken, to their heart rate, to their sleeping patterns. Sensors do not, however, typically sense the social and ambient environment of the users, such as general life style attributes or information about their social network. This means that the users themselves, and the medical practitioners, privy to the wearable sensor data, only have a narrow view of the individual, limited mainly to certain aspects of their physical condition. In this paper we describe a number of use cases for how social media can be used to complement the check-up data and those from sensors to gain a more holistic view on individuals' health, a perspective we call the 360 Quantified Self. Health-related information can be obtained from sources as diverse as food photo sharing, location check-ins, or profile pictures. Additionally, information from a person's ego network can shed light on the social dimension of wellbeing which is widely acknowledged to be of utmost importance, even though they are currently rarely used for medical diagnosis. We articulate a long-term vision describing the desirable list of technical advances and variety of data to achieve an integrated system encompassing Electronic Health Records (EHR), data from wearable devices, alongside information derived from social media data.Comment: QCRI Technical Repor

Social Bots for Online Public Health Interventions

According to the Center for Disease Control and Prevention, in the United States hundreds of thousands initiate smoking each year, and millions live with smoking-related dis- eases. Many tobacco users discuss their habits and preferences on social media. This work conceptualizes a framework for targeted health interventions to inform tobacco users about the consequences of tobacco use. We designed a Twitter bot named Notobot (short for No-Tobacco Bot) that leverages machine learning to identify users posting pro-tobacco tweets and select individualized interventions to address their interest in tobacco use. We searched the Twitter feed for tobacco-related keywords and phrases, and trained a convolutional neural network using over 4,000 tweets dichotomously manually labeled as either pro- tobacco or not pro-tobacco. This model achieves a 90% recall rate on the training set and 74% on test data. Users posting pro- tobacco tweets are matched with former smokers with similar interests who posted anti-tobacco tweets. Algorithmic matching, based on the power of peer influence, allows for the systematic delivery of personalized interventions based on real anti-tobacco tweets from former smokers. Experimental evaluation suggests that our system would perform well if deployed. This research offers opportunities for public health researchers to increase health awareness at scale. Future work entails deploying the fully operational Notobot system in a controlled experiment within a public health campaign

Diabetes primary prevention program: new insights from data analysis of recruitment period

Primary Prevention of Diabetes Program in Buenos Aires Province evaluates the effectiveness of adopting healthy lifestyle to prevent type 2 diabetes (T2D) in people at high risk of developing it. We aimed to present preliminary data analysis of FINDRISC and laboratory measurements taken during recruitment of people for the Primary Prevention of Diabetes Program in Buenos Aires Province in the cities of La Plata, Berisso, and Ensenada, Argentina.Fil: Gagliardino, Juan Jose. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - la Plata. Centro de Endocrinología Experimental y Aplicada. Universidad Nacional de la Plata. Facultad de Cs.médicas. Centro de Endocrinología Experimental y Aplicada; ArgentinaFil: Elgart, Jorge Federico. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - la Plata. Centro de Endocrinología Experimental y Aplicada. Universidad Nacional de la Plata. Facultad de Cs.médicas. Centro de Endocrinología Experimental y Aplicada; ArgentinaFil: Bourgeois, Marcelo Javier. Universidad Nacional de la Plata. Facultad de Cs.médicas. Centro Interdisc.universitario Para la Salud; ArgentinaFil: Etchegoyen, Graciela Susana. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - la Plata. Centro de Endocrinología Experimental y Aplicada. Universidad Nacional de la Plata. Facultad de Cs.médicas. Centro de Endocrinología Experimental y Aplicada; ArgentinaFil: Fantuzzi, Gabriel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - la Plata. Centro de Endocrinología Experimental y Aplicada. Universidad Nacional de la Plata. Facultad de Cs.médicas. Centro de Endocrinología Experimental y Aplicada; ArgentinaFil: Ré, Matias. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - la Plata. Centro de Endocrinología Experimental y Aplicada. Universidad Nacional de la Plata. Facultad de Cs.médicas. Centro de Endocrinología Experimental y Aplicada; ArgentinaFil: Ricart, Juan P.. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - la Plata. Centro de Endocrinología Experimental y Aplicada. Universidad Nacional de la Plata. Facultad de Cs.médicas. Centro de Endocrinología Experimental y Aplicada; ArgentinaFil: García, Silvia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - la Plata. Centro de Endocrinología Experimental y Aplicada. Universidad Nacional de la Plata. Facultad de Cs.médicas. Centro de Endocrinología Experimental y Aplicada; ArgentinaFil: Giampieri, Cecilia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - la Plata. Centro de Endocrinología Experimental y Aplicada. Universidad Nacional de la Plata. Facultad de Cs.médicas. Centro de Endocrinología Experimental y Aplicada; ArgentinaFil: Gonzalez, Lorena. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - la Plata. Centro de Endocrinología Experimental y Aplicada. Universidad Nacional de la Plata. Facultad de Cs.médicas. Centro de Endocrinología Experimental y Aplicada; ArgentinaFil: Suárez Crivaro, Florencia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - la Plata. Centro de Endocrinología Experimental y Aplicada. Universidad Nacional de la Plata. Facultad de Cs.médicas. Centro de Endocrinología Experimental y Aplicada; ArgentinaFil: Kronsbein, Peter. Niederrhein University of Applied Sciences Mönchengladbach; AlemaniaFil: Angelini, Julieta M.. Universidad Nacional de La Plata; ArgentinaFil: Martinez, Camilo. Universidad Nacional de La Plata; ArgentinaFil: Martinez, Jorge. Universidad Nacional de La Plata; ArgentinaFil: Ricart, Alberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones en Humanidades y Ciencias Sociales. Universidad Nacional de La Plata. Facultad de Humanidades y Ciencias de la Educación. Instituto de Investigaciones en Humanidades y Ciencias Sociales; ArgentinaFil: Spinedi, Eduardo Julio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - la Plata. Centro de Endocrinología Experimental y Aplicada. Universidad Nacional de la Plata. Facultad de Cs.médicas. Centro de Endocrinología Experimental y Aplicada; Argentin

Use of m-Health Technology for Preventive Interventions to Tackle Cardiometabolic Conditions and Other Non-Communicable Diseases in Latin America- Challenges and Opportunities

In Latin America, cardiovascular disease (CVD) mortality rates will increase by an estimated 145% from 1990 to 2020. Several challenges related to social strains, inadequate public health infrastructure, and underfinanced healthcare systems make cardiometabolic conditions and non-communicable diseases (NCDs) difficult to prevent and control. On the other hand, the region has high mobile phone coverage, making mobile health (mHealth) particularly attractive to complement and improve strategies toward prevention and control of these conditions in low- and middle-income countries. In this article, we describe the experiences of three Centers of Excellence for prevention and control of NCDs sponsored by the National Heart, Lung, and Blood Institute with mHealth interventions to address cardiometabolic conditions and other NCDs in Argentina, Guatemala, and Peru. The nine studies described involved the design and implementation of complex interventions targeting providers, patients and the public. The rationale, design of the interventions, and evaluation of processes and outcomes of each of these studies are described, together with barriers and enabling factors associated with their implementation.Fil: Beratarrechea, Andrea Gabriela. Instituto de Efectividad Clínica y Sanitaria; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Diez Canseco, Francisco. Universidad Peruana Cayetano Heredia; PerúFil: Irazola, Vilma. Instituto de Efectividad Clínica y Sanitaria; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Miranda, Jaime. Universidad Peruana Cayetano Heredia; PerúFil: Ramirez Zea, Manuel. Institute of Nutrition of Central America and Panama; GuatemalaFil: Rubinstein, Adolfo Luis. Instituto de Efectividad Clínica y Sanitaria; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentin