104,308 research outputs found
Social Bootstrapping: How Pinterest and Last.fm Social Communities Benefit by Borrowing Links from Facebook
How does one develop a new online community that is highly engaging to each
user and promotes social interaction? A number of websites offer friend-finding
features that help users bootstrap social networks on the website by copying
links from an established network like Facebook or Twitter. This paper
quantifies the extent to which such social bootstrapping is effective in
enhancing a social experience of the website. First, we develop a stylised
analytical model that suggests that copying tends to produce a giant connected
component (i.e., a connected community) quickly and preserves properties such
as reciprocity and clustering, up to a linear multiplicative factor. Second, we
use data from two websites, Pinterest and Last.fm, to empirically compare the
subgraph of links copied from Facebook to links created natively. We find that
the copied subgraph has a giant component, higher reciprocity and clustering,
and confirm that the copied connections see higher social interactions.
However, the need for copying diminishes as users become more active and
influential. Such users tend to create links natively on the website, to users
who are more similar to them than their Facebook friends. Our findings give new
insights into understanding how bootstrapping from established social networks
can help engage new users by enhancing social interactivity.Comment: Proc. 23rd International World Wide Web Conference (WWW), 201
Evolution of Ego-networks in Social Media with Link Recommendations
Ego-networks are fundamental structures in social graphs, yet the process of
their evolution is still widely unexplored. In an online context, a key
question is how link recommender systems may skew the growth of these networks,
possibly restraining diversity. To shed light on this matter, we analyze the
complete temporal evolution of 170M ego-networks extracted from Flickr and
Tumblr, comparing links that are created spontaneously with those that have
been algorithmically recommended. We find that the evolution of ego-networks is
bursty, community-driven, and characterized by subsequent phases of explosive
diameter increase, slight shrinking, and stabilization. Recommendations favor
popular and well-connected nodes, limiting the diameter expansion. With a
matching experiment aimed at detecting causal relationships from observational
data, we find that the bias introduced by the recommendations fosters global
diversity in the process of neighbor selection. Last, with two link prediction
experiments, we show how insights from our analysis can be used to improve the
effectiveness of social recommender systems.Comment: Proceedings of the 10th ACM International Conference on Web Search
and Data Mining (WSDM 2017), Cambridge, UK. 10 pages, 16 figures, 1 tabl
The Lifecycle and Cascade of WeChat Social Messaging Groups
Social instant messaging services are emerging as a transformative form with
which people connect, communicate with friends in their daily life - they
catalyze the formation of social groups, and they bring people stronger sense
of community and connection. However, research community still knows little
about the formation and evolution of groups in the context of social messaging
- their lifecycles, the change in their underlying structures over time, and
the diffusion processes by which they develop new members. In this paper, we
analyze the daily usage logs from WeChat group messaging platform - the largest
standalone messaging communication service in China - with the goal of
understanding the processes by which social messaging groups come together,
grow new members, and evolve over time. Specifically, we discover a strong
dichotomy among groups in terms of their lifecycle, and develop a separability
model by taking into account a broad range of group-level features, showing
that long-term and short-term groups are inherently distinct. We also found
that the lifecycle of messaging groups is largely dependent on their social
roles and functions in users' daily social experiences and specific purposes.
Given the strong separability between the long-term and short-term groups, we
further address the problem concerning the early prediction of successful
communities. In addition to modeling the growth and evolution from group-level
perspective, we investigate the individual-level attributes of group members
and study the diffusion process by which groups gain new members. By
considering members' historical engagement behavior as well as the local social
network structure that they embedded in, we develop a membership cascade model
and demonstrate the effectiveness by achieving AUC of 95.31% in predicting
inviter, and an AUC of 98.66% in predicting invitee.Comment: 10 pages, 8 figures, to appear in proceedings of the 25th
International World Wide Web Conference (WWW 2016
Social and place-focused communities in location-based online social networks
Thanks to widely available, cheap Internet access and the ubiquity of
smartphones, millions of people around the world now use online location-based
social networking services. Understanding the structural properties of these
systems and their dependence upon users' habits and mobility has many potential
applications, including resource recommendation and link prediction. Here, we
construct and characterise social and place-focused graphs by using
longitudinal information about declared social relationships and about users'
visits to physical places collected from a popular online location-based social
service. We show that although the social and place-focused graphs are
constructed from the same data set, they have quite different structural
properties. We find that the social and location-focused graphs have different
global and meso-scale structure, and in particular that social and
place-focused communities have negligible overlap. Consequently, group
inference based on community detection performed on the social graph alone
fails to isolate place-focused groups, even though these do exist in the
network. By studying the evolution of tie structure within communities, we show
that the time period over which location data are aggregated has a substantial
impact on the stability of place-focused communities, and that information
about place-based groups may be more useful for user-centric applications than
that obtained from the analysis of social communities alone.Comment: 11 pages, 5 figure
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
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“Fairy rings” of participation: the invisible network influencing participation in online communities
Individuals participate in many different ways in online communities. There is an extensive body of research describing participation as a key metaphor in communities of practice and stressing that participatory mobility is influenced by underground multidirectional activities, directed away from the notion of periphery to the centre practices and taking the shape of expansive swarming and multidirectional pulsations. This article describes an ongoing observational study proposing a model that attempts to determine how users participate in online communities and what influences them to alter the way in which they participate. We performed daily observations on user participatory behaviour in 50 online communities using public domain – anonymous data available in the communities. The specific communities were selected because they are related to learning and support learning activities within their networks. The data observations collected were analysed using Compendium, a hypermedia knowledge mapping and sense-making tool, to represent and structure the data, make complex cross data queries, test hypotheses and build representation of real examples to support our claims. Initial findings indicate that users connect, participate, contribute and collaborate on a shared objective, transferring information and pooling knowledge within and between communities in four different modes. During their online journey, users switched between modes of participation or even remained in one specific mode, implying that the way in which users participate in an online community is not just related to the mode of participation and the level of engagement with the community but it is also due to hidden reasons or motivations, an invisible network of interactions of elements that affect the willingness of the user to participate. This layer is not immediately evident in the user actions but can be inferred by analysing user reactions. It is argued that user participation in online communities occurs in two layers; the “visible” layer of participation with the different modes; and the “invisible” layer of element interactions, similar to formations observed in nature when a radically spreading underground network of fungi activity results in a ring or arc formation of mushrooms, also known as a “fairy ring”. These underground multidirectional activities influence participation and participatory mobility. Following an open scientific inquiry approach and an open research paradigm we plan to share these observations with a wider audience of practitioners, researchers and theorists for all to test or contest our arguments, and to enrich, question, or support our model
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