TY - GEN
T1 - Predicting long-term impact of CQA posts
T2 - 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2014
AU - Yao, Yuan
AU - Tong, Hanghang
AU - Xu, Feng
AU - Lu, Jian
N1 - Copyright:
Copyright 2014 Elsevier B.V., All rights reserved.
PY - 2014
Y1 - 2014
N2 - 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.
AB - 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.
KW - impact correlation
KW - long-term impact
KW - question answering
UR - http://www.scopus.com/inward/record.url?scp=84907033466&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84907033466&partnerID=8YFLogxK
U2 - 10.1145/2623330.2623649
DO - 10.1145/2623330.2623649
M3 - Conference contribution
AN - SCOPUS:84907033466
SN - 9781450329569
T3 - Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
SP - 1496
EP - 1505
BT - KDD 2014 - Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
PB - Association for Computing Machinery
Y2 - 24 August 2014 through 27 August 2014
ER -