2014 PredictingLongTermImpactofCQAPo

• (Yao et al., 2014) ⇒ Yuan Yao, Hanghang Tong, Feng Xu, and Jian Lu. (2014). “Predicting Long-term Impact of CQA Posts: A Comprehensive Viewpoint.” In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2014) Journal. ISBN:978-1-4503-2956-9 doi:10.1145/2623330.2623649

Subject Headings:

Cited By

• http://scholar.google.com/scholar?q=%222014%22+Predicting+Long-term+Impact+of+CQA+Posts%3A+A+Comprehensive+Viewpoint
• http://dl.acm.org/citation.cfm?id=2623330.2623649&preflayout=flat#citedby

Quotes

Author Keywords

• Data mining; impact correlation; long-term impact; question answering

Abstract

Community Question Answering (CQA) sites have become valuable platforms to create, share, and seek a massive volume of human knowledge. How can we spot an insightful question that would inspire massive further discussions in CQA sites? How can we detect a valuable answer that benefits many users? The long-term impact (e.g., the size of the population a post benefits) of a question / answer post is the key quantity to answer these questions. In this paper, we aim to predict the long-term impact of questions / answers shortly after they are posted in the CQA sites. In particular, we propose a family of algorithms for the prediction problem by modeling three key aspects, i.e., non-linearity, question / answer coupling, and dynamics. We analyze our algorithms in terms of optimality, correctness, and complexity. We conduct extensive experimental evaluations on two real CQA data sets to demonstrate the effectiveness and efficiency of our algorithms.

References

;

	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2014 PredictingLongTermImpactofCQAPo	Hanghang Tong Yuan Yao Feng Xu Jian Lu			Predicting Long-term Impact of CQA Posts: A Comprehensive Viewpoint				10.1145/2623330.2623649		2014