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 contributionScientificpeer-review

5 Citations (Scopus)


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 - 16th International Conference, Proceedings
PublisherSpringer Verlag
Number of pages15
ISBN (Print)9783319261898, 9783319261898
Publication statusPublished - 2015
MoE publication typeA4 Article in a conference publication
EventInternational Conference on Web Information Systems Engineering - Miami, United States
Duration: 1 Nov 20153 Nov 2015
Conference number: 16

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN (Print)03029743
ISSN (Electronic)16113349


ConferenceInternational Conference on Web Information Systems Engineering
Abbreviated titleWISE
CountryUnited States


  • Joint matrix Factorization
  • Temporal features in twitter
  • Topic derivation

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