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

Environmental accounting for ecosystem conservation: Linking societal and ecosystem metabolisms

This paper proposes an approach to environmental accounting useful for studying the feasibility of socio-economic systems in relation to the external constraints posed by ecological compatibility. The approach is based on a multi-scale analysis of the metabolic pattern of ecosystems and societies and it provides an integrated characterization of the resulting interaction. The text starts with a theoretical part explaining (i) the implicit epistemological revolution implied by the notion of ecosystem metabolism and the fund-flow model developed by Georgescu-Roegen applied to environmental accounting, and (ii) the potentials of this approach to create indicators to assess ecological integrity and environmental impacts. This revolution also makes it possible to carry out a multi-scale integrated assessment of ecosystem and societal metabolisms at the territorial level. In the second part, two applications of this approach using an indicator of the negentropic cost show the possibility to characterize in quantitative and qualitative terms degrees of alteration (crop cultivation, tree plantations)for different biomes (tropical and boreal forests). Also, a case study for land use scenarios has been included. The proposed approach represents an integrated multi-scale tool for the analysis of nature conservation scenarios and strategies.Comment: 29 pages including 6 figure

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

Bio-techno-practice. Personal and social responsibility in the academic work

The new challenges posed by biomedicine and biotechnologies ask for a deeper consideration on the relationship among science, knowledge and social responsibility. On one hand, in fact, technologies seem to shape our idea of human progress and scientific understanding of the natural world and of life in particular. On the other hand, a thoughtful consideration on the philosophical foundations of science as human enterprise is required. This also opens important questions about the new emerging paradigms of ‘excellence’ in the academic, social and market fields and on the role that universities play in training the future leaders and professionals of our society. After a short review of the contemporary philosophical reflections on the unity of knowledge, which is the origin and the goal of academic work, we argue that adherence to our current challenges through the bio-techno-practice prism is a fecund driving force of the academic activities. Moving from the experience of an international project, we also discuss the impact that such interdisciplinary activities have on what we call hidden curriculum, i.e. the embodied style of (skills that allow) people in taking care of each other in their physical, social, professional and scientific needs

Activity driven modeling of time varying networks

Network modeling plays a critical role in identifying statistical regularities and structural principles common to many systems. The large majority of recent modeling approaches are connectivity driven. The structural patterns of the network are at the basis of the mechanisms ruling the network formation. Connectivity driven models necessarily provide a time-aggregated representation that may fail to describe the instantaneous and fluctuating dynamics of many networks. We address this challenge by defining the activity potential, a time invariant function characterizing the agents' interactions and constructing an activity driven model capable of encoding the instantaneous time description of the network dynamics. The model provides an explanation of structural features such as the presence of hubs, which simply originate from the heterogeneous activity of agents. Within this framework, highly dynamical networks can be described analytically, allowing a quantitative discussion of the biases induced by the time-aggregated representations in the analysis of dynamical processes.Comment: 10 pages, 4 figure

Analytical computation of the epidemic threshold on temporal networks

The time variation of contacts in a networked system may fundamentally alter the properties of spreading processes and affect the condition for large-scale propagation, as encoded in the epidemic threshold. Despite the great interest in the problem for the physics, applied mathematics, computer science and epidemiology communities, a full theoretical understanding is still missing and currently limited to the cases where the time-scale separation holds between spreading and network dynamics or to specific temporal network models. We consider a Markov chain description of the Susceptible-Infectious-Susceptible process on an arbitrary temporal network. By adopting a multilayer perspective, we develop a general analytical derivation of the epidemic threshold in terms of the spectral radius of a matrix that encodes both network structure and disease dynamics. The accuracy of the approach is confirmed on a set of temporal models and empirical networks and against numerical results. In addition, we explore how the threshold changes when varying the overall time of observation of the temporal network, so as to provide insights on the optimal time window for data collection of empirical temporal networked systems. Our framework is both of fundamental and practical interest, as it offers novel understanding of the interplay between temporal networks and spreading dynamics.Comment: 22 pages, 6 figure