Document Type

Conference Paper

Department/Unit

Department of Computer Science

Title

A novel topical authority-based microblog ranking

Language

English

Abstract

The high volume of microblogs produced daily together with their rich social structure makes microblogs’ better query and filtering ever challenging. In the literature, most of the existing ranking methods are based on the overall popularity of the authors and the tweets without considering author’s expertise. In this paper, we propose the topical authority-based ranking methods for social networks like Twitter and investigate how the underlying topical feature modeling can be optimized for performance boosting. In particular, we present a detailed study on the empirical distributions of the topical features. We propose the use of specific parametric forms for different features, which we believe to be crucial as the value of the cumulative distribution function is explicitly used for topical authority ranking. We applied the extended topical authority-based ranking method to a Twitter dataset for ranking keyword-matched microblogs. The experimental results show that our proposed approach outperforms a number of existing approaches by a large margin which verify the effectiveness of our proposed features and the importance of the topical authority for ranking microblogs.

Keywords

Topical Authority, Feature Distribution, Microblog Ranking

Publication Date

9-2014

Source Publication Title

APWeb 2014: Web Technologies and Applications

Editors

Chen, Lei ; Jia, Yan ; Sellis, Timos ; Liu, Guanfeng

Start Page

105

End Page

116

Series Title

Lecture Notes in Computer Science, 8709.

Conference Location

Changsha, China

Publisher

Springer

Peer Reviewed

1

Copyright

© Springer International Publishing Switzerland 2014

Funder

The work of Xin Li is partially supported by National Program on Key Basic Research Project under Grant No. 2013CB329605 and the NSFC Grant under Grant No. 61300178. The work of X. Fan is supported in part by NFSC under Grant No. 61272509 and 61120106010, BNSF under Grant No. 4132049, and Specialized Research Fund for the Doctoral Program of Higher Education under grant No. 20136118110002.

DOI

10.1007/978-3-319-11116-2_10

Link to Publisher's Edition

http://dx.doi.org/10.1007/978-3-319-11116-2_10

ISBN (print)

9783319111155

ISBN (electronic)

9783319111162

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