103,299 research outputs found
Community Aliveness: Discovering Interaction Decay Patterns in Online Social Communities
Online Social Communities (OSCs) provide a medium for connecting people,
sharing news, eliciting information, and finding jobs, among others. The
dynamics of the interaction among the members of OSCs is not always growth
dynamics. Instead, a or dynamics often
happens, which makes an OSC obsolete. Understanding the behavior and the
characteristics of the members of an inactive community help to sustain the
growth dynamics of these communities and, possibly, prevents them from being
out of service. In this work, we provide two prediction models for predicting
the interaction decay of community members, namely: a Simple Threshold Model
(STM) and a supervised machine learning classification framework. We conducted
evaluation experiments for our prediction models supported by a of decayed communities extracted from the StackExchange platform. The
results of the experiments revealed that it is possible, with satisfactory
prediction performance in terms of the F1-score and the accuracy, to predict
the decay of the activity of the members of these communities using
network-based attributes and network-exogenous attributes of the members. The
upper bound of the prediction performance of the methods we used is and
for the F1-score and the accuracy, respectively. These results indicate
that network-based attributes are correlated with the activity of the members
and that we can find decay patterns in terms of these attributes. The results
also showed that the structure of the decayed communities can be used to
support the alive communities by discovering inactive members.Comment: pre-print for the 4th European Network Intelligence Conference -
11-12 September 2017 Duisburg, German
Are All Successful Communities Alike? Characterizing and Predicting the Success of Online Communities
The proliferation of online communities has created exciting opportunities to
study the mechanisms that explain group success. While a growing body of
research investigates community success through a single measure -- typically,
the number of members -- we argue that there are multiple ways of measuring
success. Here, we present a systematic study to understand the relations
between these success definitions and test how well they can be predicted based
on community properties and behaviors from the earliest period of a community's
lifetime. We identify four success measures that are desirable for most
communities: (i) growth in the number of members; (ii) retention of members;
(iii) long term survival of the community; and (iv) volume of activities within
the community. Surprisingly, we find that our measures do not exhibit very high
correlations, suggesting that they capture different types of success.
Additionally, we find that different success measures are predicted by
different attributes of online communities, suggesting that success can be
achieved through different behaviors. Our work sheds light on the basic
understanding of what success represents in online communities and what
predicts it. Our results suggest that success is multi-faceted and cannot be
measured nor predicted by a single measurement. This insight has practical
implications for the creation of new online communities and the design of
platforms that facilitate such communities.Comment: To appear at The Web Conference 201
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 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
Location Prediction: Communities Speak Louder than Friends
Humans are social animals, they interact with different communities of
friends to conduct different activities. The literature shows that human
mobility is constrained by their social relations. In this paper, we
investigate the social impact of a person's communities on his mobility,
instead of all friends from his online social networks. This study can be
particularly useful, as certain social behaviors are influenced by specific
communities but not all friends. To achieve our goal, we first develop a
measure to characterize a person's social diversity, which we term `community
entropy'. Through analysis of two real-life datasets, we demonstrate that a
person's mobility is influenced only by a small fraction of his communities and
the influence depends on the social contexts of the communities. We then
exploit machine learning techniques to predict users' future movement based on
their communities' information. Extensive experiments demonstrate the
prediction's effectiveness.Comment: ACM Conference on Online Social Networks 2015, COSN 201
When Do People Trust Their Social Groups?
Trust facilitates cooperation and supports positive outcomes in social
groups, including member satisfaction, information sharing, and task
performance. Extensive prior research has examined individuals' general
propensity to trust, as well as the factors that contribute to their trust in
specific groups. Here, we build on past work to present a comprehensive
framework for predicting trust in groups. By surveying 6,383 Facebook Groups
users about their trust attitudes and examining aggregated behavioral and
demographic data for these individuals, we show that (1) an individual's
propensity to trust is associated with how they trust their groups, (2)
smaller, closed, older, more exclusive, or more homogeneous groups are trusted
more, and (3) a group's overall friendship-network structure and an
individual's position within that structure can also predict trust. Last, we
demonstrate how group trust predicts outcomes at both individual and group
level such as the formation of new friendship ties.Comment: CHI 201
Online help-seeking in communities of practice
Interactive online help systems are considered to be a fruitful supplement to traditional IT helpdesks, which are often overloaded. They often comprise user-generated FAQ collections playing the role of technology-based conceptual artifacts. Two main questions arise: how the conceptual artifacts should be used, and which factors influence their acceptance in a community of practice (CoP). Firstly, this paper offers a theoretical frame and a usage scenario for technology-based conceptual artifacts against the theoretical background of the academic help-seeking and CoP approach. Each of the two approaches is extensively covered by psychological and educational research literature, however their combination is not yet sufficiently investigated. Secondly, the paper proposes a research model explaining the acceptance of conceptual artifacts. The model includes users’ expectations towards the artifact, perceived social influence and users’ roles in the CoP as predictors of artifact use intention and actual usage. A correlational study conducted in an academic software users’ CoP and involving structural equations modeling validates the model, suggesting thus a research line that is worth further pursuing. For educational practice, the study suggests three ways of supporting knowledge sharing in CoPs, i.e. use of technology-based conceptual artifacts, roles and division of labor, and purposeful communication in CoPs
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