2,986 research outputs found
Loyalty in Online Communities
Loyalty is an essential component of multi-community engagement. When users
have the choice to engage with a variety of different communities, they often
become loyal to just one, focusing on that community at the expense of others.
However, it is unclear how loyalty is manifested in user behavior, or whether
loyalty is encouraged by certain community characteristics.
In this paper we operationalize loyalty as a user-community relation: users
loyal to a community consistently prefer it over all others; loyal communities
retain their loyal users over time. By exploring this relation using a large
dataset of discussion communities from Reddit, we reveal that loyalty is
manifested in remarkably consistent behaviors across a wide spectrum of
communities. Loyal users employ language that signals collective identity and
engage with more esoteric, less popular content, indicating they may play a
curational role in surfacing new material. Loyal communities have denser
user-user interaction networks and lower rates of triadic closure, suggesting
that community-level loyalty is associated with more cohesive interactions and
less fragmentation into subgroups. We exploit these general patterns to predict
future rates of loyalty. Our results show that a user's propensity to become
loyal is apparent from their first interactions with a community, suggesting
that some users are intrinsically loyal from the very beginning.Comment: Extended version of a paper appearing in the Proceedings of ICWSM
2017 (with the same title); please cite the official ICWSM versio
Mining user development signals for online community churner detection
Churners are users who stop using a given service after previously signing up. In the domain of telecommunications and video games, churners represent a loss of revenue as a user leaving indicates that they will no longer pay for the service. In the context of online community platforms (e.g., community message boards, social networking sites, question--answering systems, etc.), the churning of a user can represent different kinds of loss: of social capital, of expertise, or of a vibrant individual who is a mediator for interaction and communication. Detecting which users are likely to churn from online communities, therefore, enables community managers to offer incentives to entice those users back; as retention is less expensive than re-signing users up. In this article, we tackle the task of detecting churners on four online community platforms by mining user development signals. These signals explain how users have evolved along different dimensions (i.e., social and lexical) relative to their prior behaviour and the community in which they have interacted. We present a linear model, based upon elastic-net regularisation, that uses extracted features from the signals to detect churners. Our evaluation of this model against several state of the art baselines, including our own prior work, empirically demonstrates the superior performance that this approach achieves for several experimental settings. This article presents a novel approach to churn prediction that takes a different route from existing approaches that are based on measuring static social network properties of users (e.g., centrality, in-degree, etc.)
Beautiful and damned. Combined effect of content quality and social ties on user engagement
User participation in online communities is driven by the intertwinement of
the social network structure with the crowd-generated content that flows along
its links. These aspects are rarely explored jointly and at scale. By looking
at how users generate and access pictures of varying beauty on Flickr, we
investigate how the production of quality impacts the dynamics of online social
systems. We develop a deep learning computer vision model to score images
according to their aesthetic value and we validate its output through
crowdsourcing. By applying it to over 15B Flickr photos, we study for the first
time how image beauty is distributed over a large-scale social system.
Beautiful images are evenly distributed in the network, although only a small
core of people get social recognition for them. To study the impact of exposure
to quality on user engagement, we set up matching experiments aimed at
detecting causality from observational data. Exposure to beauty is
double-edged: following people who produce high-quality content increases one's
probability of uploading better photos; however, an excessive imbalance between
the quality generated by a user and the user's neighbors leads to a decline in
engagement. Our analysis has practical implications for improving link
recommender systems.Comment: 13 pages, 12 figures, final version published in IEEE Transactions on
Knowledge and Data Engineering (Volume: PP, Issue: 99
Finding Influential Users in Social Media Using Association Rule Learning
Influential users play an important role in online social networks since
users tend to have an impact on one other. Therefore, the proposed work
analyzes users and their behavior in order to identify influential users and
predict user participation. Normally, the success of a social media site is
dependent on the activity level of the participating users. For both online
social networking sites and individual users, it is of interest to find out if
a topic will be interesting or not. In this article, we propose association
learning to detect relationships between users. In order to verify the
findings, several experiments were executed based on social network analysis,
in which the most influential users identified from association rule learning
were compared to the results from Degree Centrality and Page Rank Centrality.
The results clearly indicate that it is possible to identify the most
influential users using association rule learning. In addition, the results
also indicate a lower execution time compared to state-of-the-art methods
Modeling Attrition in Organizations from Email Communication
Abstract—Modeling people’s online behavior in relation to their real-world social context is an interesting and important research problem. In this paper, we present our preliminary study of attrition behavior in real-world organizations based on two online datasets: a dataset from a small startup (40+ users) and a dataset from one large US company (3600+ users). The small startup dataset is collected using our privacy-preserving data logging tool, which removes personal identifiable information from content data and extracts only aggregated statistics such as word frequency counts and sentiment features. The privacy-preserving measures have enabled us to recruit participants to support this study. Correlation analysis over the startup dataset has shown that statistically there is often a change point in people’s online behavior, and data exhibits weak trends that may be manifestation of real-world attrition. Same findings are also verified in the large company dataset. Furthermore, we have trained a classifier to predict real-world attrition with a moderate accuracy of 60-65 % on the large company dataset. Given the incompleteness and noisy nature of data, the accuracy is encouraging. I
Analysis of group evolution prediction in complex networks
In the world, in which acceptance and the identification with social
communities are highly desired, the ability to predict evolution of groups over
time appears to be a vital but very complex research problem. Therefore, we
propose a new, adaptable, generic and mutli-stage method for Group Evolution
Prediction (GEP) in complex networks, that facilitates reasoning about the
future states of the recently discovered groups. The precise GEP modularity
enabled us to carry out extensive and versatile empirical studies on many
real-world complex / social networks to analyze the impact of numerous setups
and parameters like time window type and size, group detection method,
evolution chain length, prediction models, etc. Additionally, many new
predictive features reflecting the group state at a given time have been
identified and tested. Some other research problems like enriching learning
evolution chains with external data have been analyzed as well
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