74,783 research outputs found
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
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