Mining user development signals for online community churner detection

Rowe, Matthew (2016) Mining user development signals for online community churner detection. ACM Transactions on Knowledge Discovery from Data, 10 (3). ISSN 1556-472X

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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.).

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Journal Article
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ACM Transactions on Knowledge Discovery from Data
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© ACM, 2016. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in ACM Transactions on Knowledge Discovery from Data, 10, 3, 2016
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02 Nov 2015 14:38
Last Modified:
18 Sep 2023 00:56