37 research outputs found
Non-systemic transmission of tick-borne diseases: a network approach
Tick-Borne diseases can be transmitted via non-systemic (NS) transmission.
This occurs when tick gets the infection by co-feeding with infected ticks on
the same host resulting in a direct pathogen transmission between the vectors,
without infecting the host. This transmission is peculiar, as it does not
require any systemic infection of the host. The NS transmission is the main
efficient transmission for the persistence of the Tick-Borne Encephalitis virus
in nature. By describing the heterogeneous ticks aggregation on hosts through a
\hyphenation{dynamical} bipartite graphs representation, we are able to
mathematically define the NS transmission and to depict the epidemiological
conditions for the pathogen persistence. Despite the fact that the underlying
network is largely fragmented, analytical and computational results show that
the larger is the variability of the aggregation, and the easier is for the
pathogen to persist in the population.Comment: 15 pages, 4 figures, to be published in Communications in Nonlinear
Science and Numerical Simulatio
Making big data work: smart, sustainable, and safe cities
The goal of the present thematic series is to showcase some of the most relevant contributions submitted to the ‘Telecom Italia Big Data Challenge 2014’ and to provide a discussion venue about recent advances in the appplication of mobile phone and social media data to the study of individual and collective behaviors. Particular attention is devoted to data-driven studies aimed at understanding city dynamics. These studies include: modeling individual and collective traffic patterns and automatically identifying areas with traffic congestion, creating high-resolution population estimates for Milan inhabitants, clustering urban dynamics of migrants and visitors traveling to a city for business or tourism, and investigating the relationship between urban communication and urban happiness
Two Types of Social Grooming Methods depending on the Trade-off between the Number and Strength of Social Relationships
Humans use various social bonding methods known as social grooming, e.g. face
to face communication, greetings, phone, and social networking sites (SNS). SNS
have drastically decreased time and distance constraints of social grooming. In
this paper, I show that two types of social grooming (elaborate social grooming
and lightweight social grooming) were discovered in a model constructed by
thirteen communication data-sets including face to face, SNS, and Chacma
baboons. The separation of social grooming methods is caused by a difference in
the trade-off between the number and strength of social relationships. The
trade-off of elaborate social grooming is weaker than the trade-off of
lightweight social grooming. On the other hand, the time and effort of
elaborate methods are higher than lightweight methods. Additionally, my model
connects social grooming behaviour and social relationship forms with these
trade-offs. By analyzing the model, I show that individuals tend to use
elaborate social grooming to reinforce a few close relationships (e.g. face to
face and Chacma baboons). In contrast, people tend to use lightweight social
grooming to maintain many weak relationships (e.g. SNS). Humans with
lightweight methods who live in significantly complex societies use various
social grooming to effectively construct social relationships.Comment: Accepted by Royal Society Open Scienc
Quantifying Information Overload in Social Media and its Impact on Social Contagions
Information overload has become an ubiquitous problem in modern society.
Social media users and microbloggers receive an endless flow of information,
often at a rate far higher than their cognitive abilities to process the
information. In this paper, we conduct a large scale quantitative study of
information overload and evaluate its impact on information dissemination in
the Twitter social media site. We model social media users as information
processing systems that queue incoming information according to some policies,
process information from the queue at some unknown rates and decide to forward
some of the incoming information to other users. We show how timestamped data
about tweets received and forwarded by users can be used to uncover key
properties of their queueing policies and estimate their information processing
rates and limits. Such an understanding of users' information processing
behaviors allows us to infer whether and to what extent users suffer from
information overload.
Our analysis provides empirical evidence of information processing limits for
social media users and the prevalence of information overloading. The most
active and popular social media users are often the ones that are overloaded.
Moreover, we find that the rate at which users receive information impacts
their processing behavior, including how they prioritize information from
different sources, how much information they process, and how quickly they
process information. Finally, the susceptibility of a social media user to
social contagions depends crucially on the rate at which she receives
information. An exposure to a piece of information, be it an idea, a convention
or a product, is much less effective for users that receive information at
higher rates, meaning they need more exposures to adopt a particular contagion.Comment: To appear at ICSWM '1
The Role of Gender in Social Network Organization
The digital traces we leave behind when engaging with the modern world offer
an interesting lens through which we study behavioral patterns as expression of
gender. Although gender differentiation has been observed in a number of
settings, the majority of studies focus on a single data stream in isolation.
Here we use a dataset of high resolution data collected using mobile phones, as
well as detailed questionnaires, to study gender differences in a large cohort.
We consider mobility behavior and individual personality traits among a group
of more than university students. We also investigate interactions among
them expressed via person-to-person contacts, interactions on online social
networks, and telecommunication. Thus, we are able to study the differences
between male and female behavior captured through a multitude of channels for a
single cohort. We find that while the two genders are similar in a number of
aspects, there are robust deviations that include multiple facets of social
interactions, suggesting the existence of inherent behavioral differences.
Finally, we quantify how aspects of an individual's characteristics and social
behavior reveals their gender by posing it as a classification problem. We ask:
How well can we distinguish between male and female study participants based on
behavior alone? Which behavioral features are most predictive