4,001 research outputs found
Academic Performance and Behavioral Patterns
Identifying the factors that influence academic performance is an essential
part of educational research. Previous studies have documented the importance
of personality traits, class attendance, and social network structure. Because
most of these analyses were based on a single behavioral aspect and/or small
sample sizes, there is currently no quantification of the interplay of these
factors. Here, we study the academic performance among a cohort of 538
undergraduate students forming a single, densely connected social network. Our
work is based on data collected using smartphones, which the students used as
their primary phones for two years. The availability of multi-channel data from
a single population allows us to directly compare the explanatory power of
individual and social characteristics. We find that the most informative
indicators of performance are based on social ties and that network indicators
result in better model performance than individual characteristics (including
both personality and class attendance). We confirm earlier findings that class
attendance is the most important predictor among individual characteristics.
Finally, our results suggest the presence of strong homophily and/or peer
effects among university students
Fundamental structures of dynamic social networks
Social systems are in a constant state of flux with dynamics spanning from
minute-by-minute changes to patterns present on the timescale of years.
Accurate models of social dynamics are important for understanding spreading of
influence or diseases, formation of friendships, and the productivity of teams.
While there has been much progress on understanding complex networks over the
past decade, little is known about the regularities governing the
micro-dynamics of social networks. Here we explore the dynamic social network
of a densely-connected population of approximately 1000 individuals and their
interactions in the network of real-world person-to-person proximity measured
via Bluetooth, as well as their telecommunication networks, online social media
contacts, geo-location, and demographic data. These high-resolution data allow
us to observe social groups directly, rendering community detection
unnecessary. Starting from 5-minute time slices we uncover dynamic social
structures expressed on multiple timescales. On the hourly timescale, we find
that gatherings are fluid, with members coming and going, but organized via a
stable core of individuals. Each core represents a social context. Cores
exhibit a pattern of recurring meetings across weeks and months, each with
varying degrees of regularity. Taken together, these findings provide a
powerful simplification of the social network, where cores represent
fundamental structures expressed with strong temporal and spatial regularity.
Using this framework, we explore the complex interplay between social and
geospatial behavior, documenting how the formation of cores are preceded by
coordination behavior in the communication networks, and demonstrating that
social behavior can be predicted with high precision.Comment: Main Manuscript: 16 pages, 4 figures. Supplementary Information: 39
pages, 34 figure
Location Prediction: Communities Speak Louder than Friends
Humans are social animals, they interact with different communities of
friends to conduct different activities. The literature shows that human
mobility is constrained by their social relations. In this paper, we
investigate the social impact of a person's communities on his mobility,
instead of all friends from his online social networks. This study can be
particularly useful, as certain social behaviors are influenced by specific
communities but not all friends. To achieve our goal, we first develop a
measure to characterize a person's social diversity, which we term `community
entropy'. Through analysis of two real-life datasets, we demonstrate that a
person's mobility is influenced only by a small fraction of his communities and
the influence depends on the social contexts of the communities. We then
exploit machine learning techniques to predict users' future movement based on
their communities' information. Extensive experiments demonstrate the
prediction's effectiveness.Comment: ACM Conference on Online Social Networks 2015, COSN 201
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
Modeling Infection with Multi-agent Dynamics
Developing the ability to comprehensively study infections in small
populations enables us to improve epidemic models and better advise individuals
about potential risks to their health. We currently have a limited
understanding of how infections spread within a small population because it has
been difficult to closely track an infection within a complete community. The
paper presents data closely tracking the spread of an infection centered on a
student dormitory, collected by leveraging the residents' use of cellular
phones. The data are based on daily symptom surveys taken over a period of four
months and proximity tracking through cellular phones. We demonstrate that
using a Bayesian, discrete-time multi-agent model of infection to model
real-world symptom reports and proximity tracking records gives us important
insights about infec-tions in small populations
Multivariate Spatiotemporal Hawkes Processes and Network Reconstruction
There is often latent network structure in spatial and temporal data and the
tools of network analysis can yield fascinating insights into such data. In
this paper, we develop a nonparametric method for network reconstruction from
spatiotemporal data sets using multivariate Hawkes processes. In contrast to
prior work on network reconstruction with point-process models, which has often
focused on exclusively temporal information, our approach uses both temporal
and spatial information and does not assume a specific parametric form of
network dynamics. This leads to an effective way of recovering an underlying
network. We illustrate our approach using both synthetic networks and networks
constructed from real-world data sets (a location-based social media network, a
narrative of crime events, and violent gang crimes). Our results demonstrate
that, in comparison to using only temporal data, our spatiotemporal approach
yields improved network reconstruction, providing a basis for meaningful
subsequent analysis --- such as community structure and motif analysis --- of
the reconstructed networks
Best friends: children use mutual gaze to identify friendships in others
This study examined childrenâs ability to use mutual eye gaze as a cue to friendships in others. In Experiment 1, following a discussion about friendship, 4-, 5-, and 6-year-olds were shown animations in which three cartoon children looked at one another, and were told that one target character had a best friend. Although all age groups accurately detected the mutual gaze between the target and another character, only 5- and 6-year-olds used this cue to infer friendship. Experiment 2 replicated the effect with 5- and 6-year-olds when the target character was not explicitly identified. Finally, in Experiment 3, where the attribution of friendship could only be based on synchronized mutual gaze, 6-year-olds made this attribution, while 4- and 5-year-olds did not. Children occasionally referred to mutual eye gaze when asked to justify their responses in Experiments 2 and 3, but it was only by the age of 6 that reference to these cues correlated with the use of mutual gaze in judgements of affiliation. Although younger children detected mutual gaze, it was not until 6 years of age that children reliably detected and justified mutual gaze as a cue to friendship
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