Predicting Churn Of Expert Respondents In Social networks

As my research covers the use of social networks and persuasive technology to improve e-commerce businesses, I wrote a paper on predicting churn of expert respondents in social networks: a case study of StackOverflow. The idea behind this was three fold: 1)identify the experts in a typical Q&A network who answer most of the questions in order to keep the network active. 2)predict the probability of them churning (if they'll remain with the network or will leave) 3) suggest and implement persuasive technology that can successfully influence possible churners in the network and make them stay(work in progress).

The paper was accepted at the 14th IEEE International Conference on Machine Learning and Applications. Yours truly was at the sunshine state to present the paper.

The abstract is below.

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.