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