Churn in Social Networks: A Discussion Boards Case Study

Karnstedt, Marcel; Hennessy, Tara; Chan, Jeffrey; Hayes, Conor

doi:10.1109/socialcom.2010.40

Cited by 28 publications

(18 citation statements)

References 17 publications

(30 reference statements)

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“…Most relevant to our work is the hazard-based modeling of Kapoor et al [19] on LASTFM. Other related work includes user engagement patterns in social networks [20,35].…”

Section: Related Workmentioning

confidence: 99%

Modeling User Consumption Sequences

Benson

Kumar

Tomkins

2016

Proceedings of the 25th International Conference on World Wide Web

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We study sequences of consumption in which the same item may be consumed multiple times. We identify two macroscopic behavior patterns of repeated consumptions. First, in a given user's lifetime, very few items live for a long time. Second, the last consumptions of an item exhibit growing inter-arrival gaps consistent with the notion of increasing boredom leading up to eventual abandonment.We then present what is to our knowledge the first holistic model of sequential repeated consumption, covering all observed aspects of this behavior. Our simple and purely combinatorial model includes no planted notion of lifetime distributions or user boredom; nonetheless, the model correctly predicts both of these phenomena. Further, we provide theoretical analysis of the behavior of the model confirming these phenomena. Additionally, the model quantitatively matches a number of microscopic phenomena across a broad range of datasets.Intriguingly, these findings suggest that the observation in a variety of domains of increasing user boredom leading to abandonment may be explained simply by probabilistic conditioning on an extinction event in a simple model, without resort to explanations based on complex human dynamics.

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“…Most relevant to our work is the hazard-based modeling of Kapoor et al [19] on LASTFM. Other related work includes user engagement patterns in social networks [20,35].…”

Section: Related Workmentioning

confidence: 99%

Modeling User Consumption Sequences

Benson

Kumar

Tomkins

2016

Proceedings of the 25th International Conference on World Wide Web

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“…Much research has been carried out on analyzing social media channels with different perspectives in mind; privacy issues [10], [11], influential users within a community [12], [13], [14], churns in online social communities [15], trending topic classifications [16], [17] etc. In the last few years, research has also started to emerge on analyzing the activities of software developers with respect to software projects on social media channels.…”

Section: Related Workmentioning

confidence: 99%

On the Need of Integrating Social Media Channels and Open Source Software Repositories

Iqbal

Decker

2015

2015 IEEE International Conference on Information Reuse and Integration

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Abstract-The growing interest in the usage of social media channels have attracted the open source software community to adopt an identity in order to disseminate project-related information to a wider audience. We foresee the need to integrate social media channels and open source software repositories in order to get an integrated view on the software project not only from the software development perspective but also from social perspective. Therefore, in this paper we study the usage of Twitter by software developers through harvesting their project-related activities on Twitter. In particular, we present the most commonly used hashtags by software developers and further investigate if project-related hashtags are the most frequent and commonly used hashtags by software developers. Based on our findings, we argue that relevant information from social media channels should be integrated with the open source software repositories in order to provide a homogeneous view on a software project.

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“…It is also worth noting that our proposed churn definition is simpler and more robust than the definition in [5], which is based on two time windows: a previous activity window and a churn window. That is, a user is labeled as churner if her activity in the churn window drops to some extent relative to the activity in the previous window.…”

Section: B Problem Formulationmentioning

confidence: 99%

“…Studies have shown that churn can be attributed to intrinsic and extrinsic factors [5], [6]; the former pertain to the customer profiles and/or inherent features of the service (such as customer's membership age, pricing, service failure rate, etc. ), while the latter portray the service in terms of the value it accrues through its social roles (e.g., community opinion, word-of-mouth effects, etc).…”

Section: Introductionmentioning

confidence: 99%

Collective Churn Prediction in Social Network

Oentaryo

Lim

et al. 2012

2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining

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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 extrinsic 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.

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Churn in Social Networks: A Discussion Boards Case Study

Cited by 28 publications

References 17 publications

Modeling User Consumption Sequences

Modeling User Consumption Sequences

On the Need of Integrating Social Media Channels and Open Source Software Repositories

Collective Churn Prediction in Social Network

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