2,814 research outputs found
Collective churn prediction in social network
Abstract—In service-based industries, churn poses a significant threat to the integrity of the user communities and profitability of the service providers. As such, research on churn prediction methods has been actively pursued, involving either intrinsic, user profile factors or extrinsic, social factors. However, existing approaches often address each type of factors separately, thus lacking a comprehensive view of churn behaviors. In this paper, we propose a new churn prediction approach based on collective classification (CC), which accounts for both the intrinsic and ex-trinsic factors by utilizing the local features of, and dependencies among, individuals during prediction steps. We evaluate our CC approach using real data provided by an established mobile social networking site, with a primary focus on prediction of churn in chat activities. Our results demonstrate that using CC and social features derived from interaction records and network structure yields substantially improved prediction in comparison to using conventional classification and user profile features only. I
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
Recommended from our members
A Large-Scale Study of Modern Code Review and Security in Open Source Projects.
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
Research on E-commerce Customer Churn Prediction Based on Improved Value Model and XG-Boost Algorithm
In recent years, with the development of Internet technology, the market competition is fiercer, the cost of acquiring new customers is increasing, and the cost of maintaining old customers is far less than the cost of acquiring new customers. Most companies are trying to market precisely through customer segmentation in order to reduce the rate of customer churn. Aiming at the customer characteristics of social network e-commerce, this paper builds a customer value model that integrates the value of social network to help companies subdivides the customer accurately. Then we use the machine learning algorithm XG-Boost to predict the churn of customers before and after the subdivision. The research found that the prediction accuracy is higher after customer segmentation. In addition, the XG-Boost algorithm is more advantageous than other algorithms
Predicting Community Evolution in Social Networks
Nowadays, sustained development of different social media can be observed
worldwide. One of the relevant research domains intensively explored recently
is analysis of social communities existing in social media as well as
prediction of their future evolution taking into account collected historical
evolution chains. These evolution chains proposed in the paper contain group
states in the previous time frames and its historical transitions that were
identified using one out of two methods: Stable Group Changes Identification
(SGCI) and Group Evolution Discovery (GED). Based on the observed evolution
chains of various length, structural network features are extracted, validated
and selected as well as used to learn classification models. The experimental
studies were performed on three real datasets with different profile: DBLP,
Facebook and Polish blogosphere. The process of group prediction was analysed
with respect to different classifiers as well as various descriptive feature
sets extracted from evolution chains of different length. The results revealed
that, in general, the longer evolution chains the better predictive abilities
of the classification models. However, chains of length 3 to 7 enabled the
GED-based method to almost reach its maximum possible prediction quality. For
SGCI, this value was at the level of 3 to 5 last periods.Comment: Entropy 2015, 17, 1-x manuscripts; doi:10.3390/e170x000x 46 page
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
- …