Time-sensitive topic derivation in Twitter

Robertus Nugroho*, Weiliang Zhao, Jian Yang, Cecile Paris, Surya Nepal, Yan Mei

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution

5 Citations (Scopus)

Abstract

Much research has been concerned with deriving topics from Twitter and applying the outcomes in a variety of real life applications such as emergency management, business advertisements and corporate/ government communication. These activities have used mostly Twitter content to derive topics. More recently, tweet interactions have also been considered, leading to better topics. Given the dynamic aspect of Twitter, we hypothesize that temporal features could further improve topic derivation on a Twitter collection. In this paper, we first perform experiments to characterize the temporal features of the interactions in Twitter. We then propose a time-sensitive topic derivation method. The proposed method incorporates temporal features when it clusters the tweets and identifies the representative terms for each topic. Our experimental results show that the inclusion of temporal features into topic derivation results in a significant improvement for both topic clustering accuracy and topic coherence comparing to existing baseline methods.

Original languageEnglish
Title of host publicationWeb Information Systems Engineering – WISE 2015
Subtitle of host publication16th International Conference, Miami, FL, USA, November 1-3, 2015, Proceedings, Part 1
EditorsJianyong Wang, Wojciech Cellary, Dingding Wang, Hua Wang, Shu-Ching Chen, Tao Li, Yanchun Zhang
Place of PublicationCham
PublisherSpringer, Springer Nature
Pages138-152
Number of pages15
ISBN (Electronic)9783319261904
ISBN (Print)9783319261898
DOIs
Publication statusPublished - 2015
Event16th International Conference on Web Information Systems Engineering, WISE 2015 - Miami, United States
Duration: 1 Nov 20153 Nov 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer International Publishing
Volume9418
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other16th International Conference on Web Information Systems Engineering, WISE 2015
CountryUnited States
CityMiami
Period1/11/153/11/15

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    Cite this

    Nugroho, R., Zhao, W., Yang, J., Paris, C., Nepal, S., & Mei, Y. (2015). Time-sensitive topic derivation in Twitter. In J. Wang, W. Cellary, D. Wang, H. Wang, S-C. Chen, T. Li, & Y. Zhang (Eds.), Web Information Systems Engineering – WISE 2015: 16th International Conference, Miami, FL, USA, November 1-3, 2015, Proceedings, Part 1 (pp. 138-152). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9418). Cham: Springer, Springer Nature. https://doi.org/10.1007/978-3-319-26190-4_10