27,384 research outputs found
Online Popularity and Topical Interests through the Lens of Instagram
Online socio-technical systems can be studied as proxy of the real world to
investigate human behavior and social interactions at scale. Here we focus on
Instagram, a media-sharing online platform whose popularity has been rising up
to gathering hundred millions users. Instagram exhibits a mixture of features
including social structure, social tagging and media sharing. The network of
social interactions among users models various dynamics including
follower/followee relations and users' communication by means of
posts/comments. Users can upload and tag media such as photos and pictures, and
they can "like" and comment each piece of information on the platform. In this
work we investigate three major aspects on our Instagram dataset: (i) the
structural characteristics of its network of heterogeneous interactions, to
unveil the emergence of self organization and topically-induced community
structure; (ii) the dynamics of content production and consumption, to
understand how global trends and popular users emerge; (iii) the behavior of
users labeling media with tags, to determine how they devote their attention
and to explore the variety of their topical interests. Our analysis provides
clues to understand human behavior dynamics on socio-technical systems,
specifically users and content popularity, the mechanisms of users'
interactions in online environments and how collective trends emerge from
individuals' topical interests.Comment: 11 pages, 11 figures, Proceedings of ACM Hypertext 201
Modeling and Predicting Popularity Dynamics via Reinforced Poisson Processes
An ability to predict the popularity dynamics of individual items within a
complex evolving system has important implications in an array of areas. Here
we propose a generative probabilistic framework using a reinforced Poisson
process to model explicitly the process through which individual items gain
their popularity. This model distinguishes itself from existing models via its
capability of modeling the arrival process of popularity and its remarkable
power at predicting the popularity of individual items. It possesses the
flexibility of applying Bayesian treatment to further improve the predictive
power using a conjugate prior. Extensive experiments on a longitudinal citation
dataset demonstrate that this model consistently outperforms existing
popularity prediction methods.Comment: 8 pages, 5 figure; 3 table
Making "fetch" happen: The influence of social and linguistic context on nonstandard word growth and decline
In an online community, new words come and go: today's "haha" may be replaced
by tomorrow's "lol." Changes in online writing are usually studied as a social
process, with innovations diffusing through a network of individuals in a
speech community. But unlike other types of innovation, language change is
shaped and constrained by the system in which it takes part. To investigate the
links between social and structural factors in language change, we undertake a
large-scale analysis of nonstandard word growth in the online community Reddit.
We find that dissemination across many linguistic contexts is a sign of growth:
words that appear in more linguistic contexts grow faster and survive longer.
We also find that social dissemination likely plays a less important role in
explaining word growth and decline than previously hypothesized
Overview Chapter 6: The diverse faces of the Second Demographic Transition in Europe
This chapter discusses the concept of the second demographic transition (SDT) and its relevance for explaining the ongoing changes in family and fertility patterns across Europe. It takes a closer look at the shifts in values and attitudes related to family, reproduction, and children, and their representation in different chapters in this collection. It re-examines the link between the second demographic transition and fertility, highlights its strong positive association with fertility at later childbearing ages, and suggests that the transition does not necessarily lead to sub-replacement fertility levels. Subsequently, it provides an extensive discussion on the progression of the SDT behind the former ‘Iron Curtain.’ To explain some apparent contradictions in this process, it employs a conceptual model of ‘readiness, willingness, and ability’ (RWA) advocated by Lesthaeghe and Vanderhoeft (2001). It also explores the multifaceted nature of the second demographic transition between different social groups, and points out an apparent paradox: whereas lower-educated individuals often embrace values that can be characterised as rather traditional, they also frequently manifest family behaviour associated with the transition, such as non-marital childbearing, high partnership instability, and high prevalence of long-term cohabitation. This suggests that there may be two different pathways of the progression of the second demographic transition. The concluding section points out the role of structural constraints for the diffusion of the transition among disadvantaged social strata, highlights the importance of the ‘gender revolution’ for the SDT trends, and discusses the usefulness of the SDT framework.Europe, family, family change, fertility, second demographic transition, values
From linguistic innovation in blogs to language learning in adults : what do interaction networks tell us?
Social networks have been found to play an increasing role in human behaviour and even the attainment of individuals. We present the results of two projects applying SNA to language phenomena. One involves exploring the social propagation of ne ologisms in a social software
(microblogging service), the other investigating the impact of
social network structure and peer interaction dynamics on
second-language learning outcomes in the setting of naturally
occurring face-to-face interaction. From local, low-level
interactions between agents verbally communicating with one
another we aim to describe the processes underlying the
emergence of more global systemic order and dynamics, using
the latest methods of complexity science.
In the former study, we demonstrate 1) the emergence of a
linguistic norm, 2) that the general lexical innovativeness of
Internet users scales not like a power law, but a unimodal, 3)
that the exposure thresholds necessary for a user to adopt new
lexemes from his/her neighbours concentrate at low values,
suggesting that—at least in low-stakes scenarios—people are
more susceptible to social influence than may erstwhile have
been expected, and 4) that, contrary to common expectations,
the most popular tags are characterised by high adoption
thresholds. In the latter, we find 1) that the best predictor of
performance is reciprocal interactions between individuals in
the language being acquired, 2) that outgoing interactions in
the acquired language are a better predictor than incoming
interactions, and 3) not surprisingly, a clear negative
relationship between performance and the intensity of
interactions with same-native-language speakers. We also
compare models where social interactions are weighted by
homophily with those that treat them as orthogonal to each
other
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