Pal et al CIKM 2010

This a Paper discussed in Social Media Analysis 10-802 in Fall 2012.

Citation

Expert Identification in Community Question Answering: Exploring Question Selection Bias. Aditya Pal, Joseph A. Konstan. In Proceedings of CIKM 2010, pages 1505-1508.

Online version

Expert Identification in Community Question Answering: Exploring Question Selection Bias

Summary

This paper presents the concept of question selection bias as a new measure to study the behavior of users in CQA. This bias provides indications about users' preference to answer questions in different stages of completeness. This completeness can be measured with the status (best answer) or number of votes of its answers. The basic finding is that experts tend to pick questions with low existing completeness.

A simple mathematical model is proposed to quantitatively compute the selection bias. Using these bias values as features, the authors apply machine learning (classification) methods to distinguish experts and ordinary users. Experiments with the TurboTax dataset show that selection bias values are superior over other types of features coming from Z-score or text analysis. Mixing up selection bias and text features provides further improvements on the classification performance. Comparison of the classifiers prove that Gaussian classification performs consistently better than linear regression and logistic regression