6,111 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
Customer churn prediction in telecom using machine learning and social network analysis in big data platform
Customer churn is a major problem and one of the most important concerns for
large companies. Due to the direct effect on the revenues of the companies,
especially in the telecom field, companies are seeking to develop means to
predict potential customer to churn. Therefore, finding factors that increase
customer churn is important to take necessary actions to reduce this churn. The
main contribution of our work is to develop a churn prediction model which
assists telecom operators to predict customers who are most likely subject to
churn. The model developed in this work uses machine learning techniques on big
data platform and builds a new way of features' engineering and selection. In
order to measure the performance of the model, the Area Under Curve (AUC)
standard measure is adopted, and the AUC value obtained is 93.3%. Another main
contribution is to use customer social network in the prediction model by
extracting Social Network Analysis (SNA) features. The use of SNA enhanced the
performance of the model from 84 to 93.3% against AUC standard. The model was
prepared and tested through Spark environment by working on a large dataset
created by transforming big raw data provided by SyriaTel telecom company. The
dataset contained all customers' information over 9 months, and was used to
train, test, and evaluate the system at SyriaTel. The model experimented four
algorithms: Decision Tree, Random Forest, Gradient Boosted Machine Tree "GBM"
and Extreme Gradient Boosting "XGBOOST". However, the best results were
obtained by applying XGBOOST algorithm. This algorithm was used for
classification in this churn predictive model.Comment: 24 pages, 14 figures. PDF https://rdcu.be/budK
Mass Customization in Wireless Communication Services: Individual Service Bundles and Tariffs
This paper presents results on mass customization of wireless communications services and tariffs. It advocates for a user-centric view of wireless service configuration and pricing as opposed to present-day service catalog options. The focus is on design methodology and tools for such individual services and tariffs, using altogether information compression, negotiation algorithms, and risk portfolio analysis. We first analyze the user and supplier needs and aspirations. We then introduce the systematic design-oriented approach which can be applied. The implications of this approach for users and suppliers are discussed based on an end-user survey and on model-based calculations. It is shown that users can achieve desired service bundle cost reduction, while suppliers can improve significantly their risk-profit equilibrium points, reduce churn and simplify provisioning.negotiation;mass customization;service configuration;mobile communication services;individual tariffs
Improving customer churn prediction by data augmentation using pictorial stimulus-choice data
The purpose of this paper is to determine the added value of pictorial stimulus-choice data in customer churn prediction. Using Random Forests and 5 times 2 fold cross-validation, this study analyzes how much pictorial stimulus choice data and survey data increase the AUC of a churn model over and above administrative, operational and complaints data. The finding is that pictorial-stimulus choice data significantly increases AUC of models with administrative and operational data. The practical implication of this finding is that companies should start considering mining pictorial data from social media sites (e.g. Pinterest), in order to augment their internal customer database. This study is original in that it is the first that assesses the added value of pictorial stimulus-choice data in predictive models. This is important because more and more social media websites are focusing on pictures
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
All Who Wander: On the Prevalence and Characteristics of Multi-community Engagement
Although analyzing user behavior within individual communities is an active
and rich research domain, people usually interact with multiple communities
both on- and off-line. How do users act in such multi-community environments?
Although there are a host of intriguing aspects to this question, it has
received much less attention in the research community in comparison to the
intra-community case. In this paper, we examine three aspects of
multi-community engagement: the sequence of communities that users post to, the
language that users employ in those communities, and the feedback that users
receive, using longitudinal posting behavior on Reddit as our main data source,
and DBLP for auxiliary experiments. We also demonstrate the effectiveness of
features drawn from these aspects in predicting users' future level of
activity.
One might expect that a user's trajectory mimics the "settling-down" process
in real life: an initial exploration of sub-communities before settling down
into a few niches. However, we find that the users in our data continually post
in new communities; moreover, as time goes on, they post increasingly evenly
among a more diverse set of smaller communities. Interestingly, it seems that
users that eventually leave the community are "destined" to do so from the very
beginning, in the sense of showing significantly different "wandering" patterns
very early on in their trajectories; this finding has potentially important
design implications for community maintainers. Our multi-community perspective
also allows us to investigate the "situation vs. personality" debate from
language usage across different communities.Comment: 11 pages, data available at
https://chenhaot.com/pages/multi-community.html, Proceedings of WWW 2015
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