37,717 research outputs found
Heterogeneity shapes groups growth in social online communities
Many complex systems are characterized by broad distributions capturing, for
example, the size of firms, the population of cities or the degree distribution
of complex networks. Typically this feature is explained by means of a
preferential growth mechanism. Although heterogeneity is expected to play a
role in the evolution it is usually not considered in the modeling probably due
to a lack of empirical evidence on how it is distributed. We characterize the
intrinsic heterogeneity of groups in an online community and then show that
together with a simple linear growth and an inhomogeneous birth rate it
explains the broad distribution of group members.Comment: 5 pages, 3 figure panel
Does sex education influence sexual and reproductive behaviour of women? Evidence from Mexico
This article examines the influence of sex education on sexual and reproductive behavior in Mexican women. Exposure to in-school sex education is identified and duration-hazard models are estimated to assess its effects on initiation of sexual activity and use of contraception methods, and timing of first and second pregnancies. Results consistently reveal that women exposed to sex education begin using contraception methods earlier. Most evidence indicates that exposed women initiate sexual activity earlier. Findings suggest that timing of first pregnancy is not affected and that second pregnancy is postponed. Overall, outcomes from this study support the idea that sex education contributes to promote preventive sexual health.Sex education; female sexual health; reproductive behavior
Academic team formation as evolving hypergraphs
This paper quantitatively explores the social and socio-semantic patterns of
constitution of academic collaboration teams. To this end, we broadly underline
two critical features of social networks of knowledge-based collaboration:
first, they essentially consist of group-level interactions which call for
team-centered approaches. Formally, this induces the use of hypergraphs and
n-adic interactions, rather than traditional dyadic frameworks of interaction
such as graphs, binding only pairs of agents. Second, we advocate the joint
consideration of structural and semantic features, as collaborations are
allegedly constrained by both of them. Considering these provisions, we propose
a framework which principally enables us to empirically test a series of
hypotheses related to academic team formation patterns. In particular, we
exhibit and characterize the influence of an implicit group structure driving
recurrent team formation processes. On the whole, innovative production does
not appear to be correlated with more original teams, while a polarization
appears between groups composed of experts only or non-experts only, altogether
corresponding to collectives with a high rate of repeated interactions
Distinguishing Topical and Social Groups Based on Common Identity and Bond Theory
Social groups play a crucial role in social media platforms because they form
the basis for user participation and engagement. Groups are created explicitly
by members of the community, but also form organically as members interact. Due
to their importance, they have been studied widely (e.g., community detection,
evolution, activity, etc.). One of the key questions for understanding how such
groups evolve is whether there are different types of groups and how they
differ. In Sociology, theories have been proposed to help explain how such
groups form. In particular, the common identity and common bond theory states
that people join groups based on identity (i.e., interest in the topics
discussed) or bond attachment (i.e., social relationships). The theory has been
applied qualitatively to small groups to classify them as either topical or
social. We use the identity and bond theory to define a set of features to
classify groups into those two categories. Using a dataset from Flickr, we
extract user-defined groups and automatically-detected groups, obtained from a
community detection algorithm. We discuss the process of manual labeling of
groups into social or topical and present results of predicting the group label
based on the defined features. We directly validate the predictions of the
theory showing that the metrics are able to forecast the group type with high
accuracy. In addition, we present a comparison between declared and detected
groups along topicality and sociality dimensions.Comment: 10 pages, 6 figures, 2 table
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