Automated Cellular Modeling and Prediction on a Large Scale
Artificial Intelligence Review - Issues on the application of data mining
Understanding churn in peer-to-peer networks
Proceedings of the 6th ACM SIGCOMM conference on Internet measurement
Expert Systems with Applications: An International Journal
Social ties and their relevance to churn in mobile telecom networks
EDBT '08 Proceedings of the 11th international conference on Extending database technology: Advances in database technology
Predicting credit card customer churn in banks using data mining
International Journal of Data Analysis Techniques and Strategies
Expert Systems with Applications: An International Journal
Churn Prediction in MMORPGs: A Social Influence Based Approach
CSE '09 Proceedings of the 2009 International Conference on Computational Science and Engineering - Volume 04
Building comprehensible customer churn prediction models with advanced rule induction techniques
Expert Systems with Applications: An International Journal
Customer churn prediction --a case study in retail banking
Proceedings of the 2010 conference on Data Mining for Business Applications
IEEE Transactions on Neural Networks
No country for old members: user lifecycle and linguistic change in online communities
Proceedings of the 22nd international conference on World Wide Web
Timespent based models for predicting user retention
Proceedings of the 22nd international conference on World Wide Web
From amateurs to connoisseurs: modeling the evolution of user expertise through online reviews
Proceedings of the 22nd international conference on World Wide Web
Proceedings of the 22nd international conference on World Wide Web
Predicting user activity level in social networks
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Social resilience in online communities: the autopsy of friendster
Proceedings of the first ACM conference on Online social networks
Behavior Analysis of Microblog Users Based on Transitions in Posting Activities
Proceedings of International Conference on Information Integration and Web-based Applications & Services
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One of the important targets of community-based question answering (CQA) services, such as Yahoo! Answers, Quora and Baidu Zhidao, is to maintain and even increase the number of active answerers, that is the users who provide answers to open questions. The reasoning is that they are the engine behind satisfied askers, which is the overall goal behind CQA. Yet, this task is not an easy one. Indeed, our empirical observation shows that many users provide just one or two answers and then leave. In this work we try to detect answerers that are about to quit, a task known as churn prediction, but unlike prior work, we focus on new users. To address the task of churn prediction in new users, we extract a variety of features to model the behavior of \YA{} users over the first week of their activity, including personal information, rate of activity, and social interaction with other users. Several classifiers trained on the data show that there is a statistically significant signal for discriminating between users who are likely to churn and those who are not. A detailed feature analysis shows that the two most important signals are the total number of answers given by the user, closely related to the motivation of the user, and attributes related to the amount of recognition given to the user, measured in counts of best answers, thumbs up and positive responses by the asker.