Predicting Churn of Expert Respondents in Social Networks Using Data Mining Techniques: A Case Study of Stack Overflow

Ifeoma Adaji · Julita Vassileva

13 citations0/0 files storedInternational Conference on Machine Learning and Applications

No readable copy stored yet — this record is metadata only.

No open-access location is known for this record yet.

Literature note Cite in a project Citation map

Customer churn and segmentation Online Learning and Analytics Expert finding and Q&A systems

Abstract

In Q&A social networks, the few respondents that answer most of the questions are an asset to that network. Being able to predict the churn of these expert respondents will enable the owners of such network put things in place in order to keep them. In this paper, we predicted the churn of expert respondents in Stack Overflow. We identified experts based on the InDegree of the respondents and the value of the incentives earned by these experts from the questions they have answered in the past. Using four data mining techniques: logistic regression, neural networks, support vector machines and random forests, we predicted user churn and evaluated our results with four evaluation metrics: percentage correctly classified, area under receiver operating characteristic curve, precision and recall. Of the four data mining algorithms, random forests performed best with PCC of 76%, ROC area of 0.82, precision of 0.76 and recall of 0.77.

Paper brief

Loading…

Cite this work

Citation unavailable for this work.

BibTeX RIS CSL-JSON

No public copy is stored for this record, so only the metadata is cited.

Citations in this library

Citation map →

Stored only when both papers are in the corpus — 13 citations exist upstream.

References

2004Customer base analysis: partial defection of behaviourally loyal clients in a non-contractual FMCG retail setting 2010Building comprehensible customer churn prediction models with advanced rule induction techniques 2005Predicting customer retention and profitability by using random forests and regression forests techniques 2006CRM at a pay-TV company: Using analytical models to reduce customer attrition by targeted marketing for subscription services 2004An integrated data mining and behavioral scoring model for analyzing bank customers 2009Cluster analysis using data mining approach to develop CRM methodology to assess the customer loyalty 2008Improving customer attrition prediction by integrating emotions from client/company interaction emails and evaluating multiple classifiers 2012Modeling partial customer churn: On the value of first product-category purchase sequences

Explore the map →

Papers that cite the same references — the relational fallback while embeddings are still backfilling.

Imported from OpenAlex (CC0) — nothing here is generated.