Detecting epidemic tendency by mining search logs

Authors:
Weize Kong;Yiqun Liu;Shaoping Ma;Liyun Ru
Affiliations:
Beihang University, Beijing, China;Tsinghua University, Beijing, China;Tsinghua University, Beijing, China;Tsinghua University, Beijing, China
Venue:
Proceedings of the 19th international conference on World wide web
Year:
2010

Citing 1
Cited 1

Query clustering using click-through graph

Proceedings of the 18th international conference on World wide web

Inferring the demographics of search users: social data meets search queries

Proceedings of the 22nd international conference on World Wide Web

Quantified Score

Hi-index	0.00

Visualization

Abstract

We consider the problem of detecting epidemic tendency by mining search logs. We propose an algorithm based on click-through information to select epidemic related queries/terms. We adopt linear regression to model epidemic occurrences and frequencies of epidemic related terms (ERTs) in search logs. The results show our algorithm is effective in finding ERTs which obtain a high correlation value with epidemic occurrences. We also find the proposed method performs better when combining different ERTs than using single ERT.