Deriving Topics in Twitter by Exploiting Tweet Interactions

Robertus Nugroho, Jian Yang, Youliang Zhong, Cecile Paris, Surya Nepal

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

12 Citations (Scopus)

Abstract

Twitter as a big data social network becomes one of the most important sources for capturing the up-To-date events happening in the world. Topic derivation from Twitter is important for various applications such as situation awareness, market analysis, content filtering, and recommendations. However, tweets are short messages, which makes topic derivation challenging. Current methods employ various semantic features of tweet content but mostly overlook the interactions among tweets. In this paper, we propose a novel topic derivation method that takes into account the interactions among tweets, defined as the reciprocal activities related to people who send the tweets, as well as actions and tweet contents. In particular, topics are derived by performing a two-step matrix factorization jointly over the interactions and semantic features of the tweets. We have conducted a number of experiments on tweets collected over a period of time, showing that the proposed method consistently outperforms other advanced topic derivation methods in the literature. Our experiments also reveal that the interactions among tweets do significantly relieve the sparsity problem caused by the short-Text nature of Twitter.

Original languageEnglish
Title of host publicationProceedings - 2015 IEEE International Congress on Big Data, BigData Congress 2015
PublisherIEEE
Pages87-94
Number of pages8
ISBN (Electronic)9781467372787
DOIs
Publication statusPublished - 17 Aug 2015
MoE publication typeA4 Article in a conference publication
EventIEEE International Conference on Big Data - New York City, United States
Duration: 27 Jun 20152 Jul 2015
Conference number: 4

Conference

ConferenceIEEE International Conference on Big Data
Abbreviated titleBigData
CountryUnited States
CityNew York City
Period27/06/201502/07/2015

Keywords

  • Interactions of Tweets
  • Joint Matrix Factorization
  • Topic Derivation
  • Twitter

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