Detecting hot topics in chinese microblog streams based on frequent patterns mining

Authors:
Weili Xu;Shi Feng;Lin Wang;Daling Wang;Ge Yu
Affiliations:
School of Information Science and Engineering, Northeastern University, P.R.China;School of Information Science and Engineering, Northeastern University, P.R.China,Key Laboratory of Medical Image Computing, Northeastern University, Ministry of Education, Shenyang, P.R.China;School of Information Science and Engineering, Northeastern University, P.R.China;School of Information Science and Engineering, Northeastern University, P.R.China,Key Laboratory of Medical Image Computing, Northeastern University, Ministry of Education, Shenyang, P.R.China;School of Information Science and Engineering, Northeastern University, P.R.China,Key Laboratory of Medical Image Computing, Northeastern University, Ministry of Education, Shenyang, P.R.China
Venue:
WISM'12 Proceedings of the 2012 international conference on Web Information Systems and Mining
Year:
2012

Citing 5
Cited 0

Fast Algorithms for Mining Association Rules in Large Databases

VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Topic detection and tracking evaluation overview

Topic detection and tracking
Identifying topical authorities in microblogs

Proceedings of the fourth ACM international conference on Web search and data mining
Topical semantics of twitter links

Proceedings of the fourth ACM international conference on Web search and data mining
Comparing twitter and traditional media using topic models

ECIR'11 Proceedings of the 33rd European conference on Advances in information retrieval

Quantified Score

Hi-index	0.00

Visualization

Abstract

Microblog plays a more and more important role on the emerging and propagation of the public opinion on the Web. Although topic detection has long been a hot research topic, the characteristics of microblog make it a non-trivial task. In this paper, we propose a novel hot topic detection approach based on keyword extraction and frequent patterns mining. We analyze the characteristics of hot topic microblogs and the topical keywords are extracted according to the increasing rate and frequency in Chinese microblog streams. Different from traditional clustering based detection methods, in this paper we treat the short texts of microblogs as transaction items, and apply Apriori algorithm to generate the hot topics. The experiments in the real dataset verify the efficiency and effectiveness of our proposed methods.