7,249 research outputs found
Postmortem Analysis of Decayed Online Social Communities: Cascade Pattern Analysis and Prediction
Recently, many online social networks, such as MySpace, Orkut, and
Friendster, have faced inactivity decay of their members, which contributed to
the collapse of these networks. The reasons, mechanics, and prevention
mechanisms of such inactivity decay are not fully understood. In this work, we
analyze decayed and alive sub-websites from the StackExchange platform. The
analysis mainly focuses on the inactivity cascades that occur among the members
of these communities. We provide measures to understand the decay process and
statistical analysis to extract the patterns that accompany the inactivity
decay. Additionally, we predict cascade size and cascade virality using machine
learning. The results of this work include a statistically significant
difference of the decay patterns between the decayed and the alive
sub-websites. These patterns are mainly: cascade size, cascade virality,
cascade duration, and cascade similarity. Additionally, the contributed
prediction framework showed satisfactory prediction results compared to a
baseline predictor. Supported by empirical evidence, the main findings of this
work are: (1) the decay process is not governed by only one network measure; it
is better described using multiple measures; (2) the expert members of the
StackExchange sub-websites were mainly responsible for the activity or
inactivity of the StackExchange sub-websites; (3) the Statistics sub-website is
going through decay dynamics that may lead to it becoming fully-decayed; and
(4) decayed sub-websites were originally less resilient to inactivity decay,
unlike the alive sub-websites
The Size Conundrum: Why Online Knowledge Markets Can Fail at Scale
In this paper, we interpret the community question answering websites on the
StackExchange platform as knowledge markets, and analyze how and why these
markets can fail at scale. A knowledge market framing allows site operators to
reason about market failures, and to design policies to prevent them. Our goal
is to provide insights on large-scale knowledge market failures through an
interpretable model. We explore a set of interpretable economic production
models on a large empirical dataset to analyze the dynamics of content
generation in knowledge markets. Amongst these, the Cobb-Douglas model best
explains empirical data and provides an intuitive explanation for content
generation through concepts of elasticity and diminishing returns. Content
generation depends on user participation and also on how specific types of
content (e.g. answers) depends on other types (e.g. questions). We show that
these factors of content generation have constant elasticity---a percentage
increase in any of the inputs leads to a constant percentage increase in the
output. Furthermore, markets exhibit diminishing returns---the marginal output
decreases as the input is incrementally increased. Knowledge markets also vary
on their returns to scale---the increase in output resulting from a
proportionate increase in all inputs. Importantly, many knowledge markets
exhibit diseconomies of scale---measures of market health (e.g., the percentage
of questions with an accepted answer) decrease as a function of number of
participants. The implications of our work are two-fold: site operators ought
to design incentives as a function of system size (number of participants); the
market lens should shed insight into complex dependencies amongst different
content types and participant actions in general social networks.Comment: The 27th International Conference on World Wide Web (WWW), 201
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
The Lifecycles of Apps in a Social Ecosystem
Apps are emerging as an important form of on-line content, and they combine
aspects of Web usage in interesting ways --- they exhibit a rich temporal
structure of user adoption and long-term engagement, and they exist in a
broader social ecosystem that helps drive these patterns of adoption and
engagement. It has been difficult, however, to study apps in their natural
setting since this requires a simultaneous analysis of a large set of popular
apps and the underlying social network they inhabit.
In this work we address this challenge through an analysis of the collection
of apps on Facebook Login, developing a novel framework for analyzing both
temporal and social properties. At the temporal level, we develop a retention
model that represents a user's tendency to return to an app using a very small
parameter set. At the social level, we organize the space of apps along two
fundamental axes --- popularity and sociality --- and we show how a user's
probability of adopting an app depends both on properties of the local network
structure and on the match between the user's attributes, his or her friends'
attributes, and the dominant attributes within the app's user population. We
also develop models that show the importance of different feature sets with
strong performance in predicting app success.Comment: 11 pages, 10 figures, 3 tables, International World Wide Web
Conferenc
On the Role of Social Identity and Cohesion in Characterizing Online Social Communities
Two prevailing theories for explaining social group or community structure
are cohesion and identity. The social cohesion approach posits that social
groups arise out of an aggregation of individuals that have mutual
interpersonal attraction as they share common characteristics. These
characteristics can range from common interests to kinship ties and from social
values to ethnic backgrounds. In contrast, the social identity approach posits
that an individual is likely to join a group based on an intrinsic
self-evaluation at a cognitive or perceptual level. In other words group
members typically share an awareness of a common category membership.
In this work we seek to understand the role of these two contrasting theories
in explaining the behavior and stability of social communities in Twitter. A
specific focal point of our work is to understand the role of these theories in
disparate contexts ranging from disaster response to socio-political activism.
We extract social identity and social cohesion features-of-interest for large
scale datasets of five real-world events and examine the effectiveness of such
features in capturing behavioral characteristics and the stability of groups.
We also propose a novel measure of social group sustainability based on the
divergence in group discussion. Our main findings are: 1) Sharing of social
identities (especially physical location) among group members has a positive
impact on group sustainability, 2) Structural cohesion (represented by high
group density and low average shortest path length) is a strong indicator of
group sustainability, and 3) Event characteristics play a role in shaping group
sustainability, as social groups in transient events behave differently from
groups in events that last longer
The Impact of Membership Overlap on the Survival of Online Communities
Online communities play an important role in society. In this paper, we study the effects of membership overlap on the survival of online communities. By analyzing the historical data of 5673 Wikia communities, we find that higher levels of membership overlap are positively associated with greater survival rate of online communities. Furthermore, we find that it is beneficial for new communities to have shared members who play a central role in other mature communities. These findings provide new insight into an important mechanism underlying successful online communities, contribute to theories of organization science, and provide several actionable steps for the hosts and creators of online communities
MoodBar: Increasing new user retention in Wikipedia through lightweight socialization
Socialization in online communities allows existing members to welcome and
recruit newcomers, introduce them to community norms and practices, and sustain
their early participation. However, socializing newcomers does not come for
free: in large communities, socialization can result in a significant workload
for mentors and is hard to scale. In this study we present results from an
experiment that measured the effect of a lightweight socialization tool on the
activity and retention of newly registered users attempting to edit for the
first time Wikipedia. Wikipedia is struggling with the retention of newcomers
and our results indicate that a mechanism to elicit lightweight feedback and to
provide early mentoring to newcomers improves their chances of becoming
long-term contributors.Comment: 9 pages, 5 figures, accepted for presentation at CSCW'1
A "Social Bitcoin" could sustain a democratic digital world
A multidimensional financial system could provide benefits for individuals,
companies, and states. Instead of top-down control, which is destined to
eventually fail in a hyperconnected world, a bottom-up creation of value can
unleash creative potential and drive innovations. Multiple currency dimensions
can represent different externalities and thus enable the design of incentives
and feedback mechanisms that foster the ability of complex dynamical systems to
self-organize and lead to a more resilient society and sustainable economy.
Modern information and communication technologies play a crucial role in this
process, as Web 2.0 and online social networks promote cooperation and
collaboration on unprecedented scales. Within this contribution, we discuss how
one dimension of a multidimensional currency system could represent
socio-digital capital (Social Bitcoins) that can be generated in a bottom-up
way by individuals who perform search and navigation tasks in a future version
of the digital world. The incentive to mine Social Bitcoins could sustain
digital diversity, which mitigates the risk of totalitarian control by powerful
monopolies of information and can create new business opportunities needed in
times where a large fraction of current jobs is estimated to disappear due to
computerisation.Comment: Contribution to EPJ-ST special issue on 'Can economics be a Physical
Science?', edited by S. Sinha, A. S. Chakrabarti & M. Mitr
A survey of statistical network models
Networks are ubiquitous in science and have become a focal point for
discussion in everyday life. Formal statistical models for the analysis of
network data have emerged as a major topic of interest in diverse areas of
study, and most of these involve a form of graphical representation.
Probability models on graphs date back to 1959. Along with empirical studies in
social psychology and sociology from the 1960s, these early works generated an
active network community and a substantial literature in the 1970s. This effort
moved into the statistical literature in the late 1970s and 1980s, and the past
decade has seen a burgeoning network literature in statistical physics and
computer science. The growth of the World Wide Web and the emergence of online
networking communities such as Facebook, MySpace, and LinkedIn, and a host of
more specialized professional network communities has intensified interest in
the study of networks and network data. Our goal in this review is to provide
the reader with an entry point to this burgeoning literature. We begin with an
overview of the historical development of statistical network modeling and then
we introduce a number of examples that have been studied in the network
literature. Our subsequent discussion focuses on a number of prominent static
and dynamic network models and their interconnections. We emphasize formal
model descriptions, and pay special attention to the interpretation of
parameters and their estimation. We end with a description of some open
problems and challenges for machine learning and statistics.Comment: 96 pages, 14 figures, 333 reference
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