403 research outputs found
Do Linguistic Style and Readability of Scientific Abstracts affect their Virality?
Reactions to textual content posted in an online social network show
different dynamics depending on the linguistic style and readability of the
submitted content. Do similar dynamics exist for responses to scientific
articles? Our intuition, supported by previous research, suggests that the
success of a scientific article depends on its content, rather than on its
linguistic style. In this article, we examine a corpus of scientific abstracts
and three forms of associated reactions: article downloads, citations, and
bookmarks. Through a class-based psycholinguistic analysis and readability
indices tests, we show that certain stylistic and readability features of
abstracts clearly concur in determining the success and viral capability of a
scientific article.Comment: Proceedings of the Sixth International AAAI Conference on Weblogs and
Social Media (ICWSM 2012), 4-8 June 2012, Dublin, Irelan
Automatic Prediction Of Small Group Performance In Information Sharing Tasks
In this paper, we describe a novel approach, based on Markov jump processes,
to model small group conversational dynamics and to predict small group
performance. More precisely, we estimate conversational events such as turn
taking, backchannels, turn-transitions at the micro-level (1 minute windows)
and then we bridge the micro-level behavior and the macro-level performance. We
tested our approach with a cooperative task, the Information Sharing task, and
we verified the relevance of micro- level interaction dynamics in determining a
good group performance (e.g. higher speaking turns rate and more balanced
participation among group members).Comment: Presented at Collective Intelligence conference, 2012
(arXiv:1204.2991
PocketCare: Tracking the Flu with Mobile Phones using Partial Observations of Proximity and Symptoms
Mobile phones provide a powerful sensing platform that researchers may adopt
to understand proximity interactions among people and the diffusion, through
these interactions, of diseases, behaviors, and opinions. However, it remains a
challenge to track the proximity-based interactions of a whole community and
then model the social diffusion of diseases and behaviors starting from the
observations of a small fraction of the volunteer population. In this paper, we
propose a novel approach that tries to connect together these sparse
observations using a model of how individuals interact with each other and how
social interactions happen in terms of a sequence of proximity interactions. We
apply our approach to track the spreading of flu in the spatial-proximity
network of a 3000-people university campus by mobilizing 300 volunteers from
this population to monitor nearby mobile phones through Bluetooth scanning and
to daily report flu symptoms about and around them. Our aim is to predict the
likelihood for an individual to get flu based on how often her/his daily
routine intersects with those of the volunteers. Thus, we use the daily
routines of the volunteers to build a model of the volunteers as well as of the
non-volunteers. Our results show that we can predict flu infection two weeks
ahead of time with an average precision from 0.24 to 0.35 depending on the
amount of information. This precision is six to nine times higher than with a
random guess model. At the population level, we can predict infectious
population in a two-week window with an r-squared value of 0.95 (a random-guess
model obtains an r-squared value of 0.2). These results point to an innovative
approach for tracking individuals who have interacted with people showing
symptoms, allowing us to warn those in danger of infection and to inform health
researchers about the progression of contact-induced diseases
Modeling Taxi Drivers' Behaviour for the Next Destination Prediction
In this paper, we study how to model taxi drivers' behaviour and geographical
information for an interesting and challenging task: the next destination
prediction in a taxi journey. Predicting the next location is a well studied
problem in human mobility, which finds several applications in real-world
scenarios, from optimizing the efficiency of electronic dispatching systems to
predicting and reducing the traffic jam. This task is normally modeled as a
multiclass classification problem, where the goal is to select, among a set of
already known locations, the next taxi destination. We present a Recurrent
Neural Network (RNN) approach that models the taxi drivers' behaviour and
encodes the semantics of visited locations by using geographical information
from Location-Based Social Networks (LBSNs). In particular, RNNs are trained to
predict the exact coordinates of the next destination, overcoming the problem
of producing, in output, a limited set of locations, seen during the training
phase. The proposed approach was tested on the ECML/PKDD Discovery Challenge
2015 dataset - based on the city of Porto -, obtaining better results with
respect to the competition winner, whilst using less information, and on
Manhattan and San Francisco datasets.Comment: preprint version of a paper submitted to IEEE Transactions on
Intelligent Transportation System
Weak nodes detection in urban transport systems: Planning for resilience in Singapore
The availability of massive data-sets describing human mobility offers the
possibility to design simulation tools to monitor and improve the resilience of
transport systems in response to traumatic events such as natural and man-made
disasters (e.g. floods terroristic attacks, etc...). In this perspective, we
propose ACHILLES, an application to model people's movements in a given
transport system mode through a multiplex network representation based on
mobility data. ACHILLES is a web-based application which provides an
easy-to-use interface to explore the mobility fluxes and the connectivity of
every urban zone in a city, as well as to visualize changes in the transport
system resulting from the addition or removal of transport modes, urban zones,
and single stops. Notably, our application allows the user to assess the
overall resilience of the transport network by identifying its weakest node,
i.e. Urban Achilles Heel, with reference to the ancient Greek mythology. To
demonstrate the impact of ACHILLES for humanitarian aid we consider its
application to a real-world scenario by exploring human mobility in Singapore
in response to flood prevention.Comment: 9 pages, 6 figures, IEEE Data Science and Advanced Analytic
Rethinking the Learning Paradigm for Facial Expression Recognition
Due to the subjective crowdsourcing annotations and the inherent inter-class
similarity of facial expressions, the real-world Facial Expression Recognition
(FER) datasets usually exhibit ambiguous annotation. To simplify the learning
paradigm, most previous methods convert ambiguous annotation results into
precise one-hot annotations and train FER models in an end-to-end supervised
manner. In this paper, we rethink the existing training paradigm and propose
that it is better to use weakly supervised strategies to train FER models with
original ambiguous annotation
A stigmergy-based analysis of city hotspots to discover trends and anomalies in urban transportation usage
A key aspect of a sustainable urban transportation system is the
effectiveness of transportation policies. To be effective, a policy has to
consider a broad range of elements, such as pollution emission, traffic flow,
and human mobility. Due to the complexity and variability of these elements in
the urban area, to produce effective policies remains a very challenging task.
With the introduction of the smart city paradigm, a widely available amount of
data can be generated in the urban spaces. Such data can be a fundamental
source of knowledge to improve policies because they can reflect the
sustainability issues underlying the city. In this context, we propose an
approach to exploit urban positioning data based on stigmergy, a bio-inspired
mechanism providing scalar and temporal aggregation of samples. By employing
stigmergy, samples in proximity with each other are aggregated into a
functional structure called trail. The trail summarizes relevant dynamics in
data and allows matching them, providing a measure of their similarity.
Moreover, this mechanism can be specialized to unfold specific dynamics.
Specifically, we identify high-density urban areas (i.e hotspots), analyze
their activity over time, and unfold anomalies. Moreover, by matching activity
patterns, a continuous measure of the dissimilarity with respect to the typical
activity pattern is provided. This measure can be used by policy makers to
evaluate the effect of policies and change them dynamically. As a case study,
we analyze taxi trip data gathered in Manhattan from 2013 to 2015.Comment: Preprin
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