The Joint effects of tweet content similarity and tweet interactions for topic derivation

Robertus Nugroho, Weiliang Zhao, Jian Yang, Cecile Paris, Surya Nepal

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

Abstract

Interactions among tweets, i.e., mentions, retweets, replies, are important factors contributing to the quality of topic derivation on Twitter. If applied correctly, the incorporation of tweet interactions can significantly improve the quality of topic derivation in comparison with approaches that are mainly based on the content similarity analysis. However, how interactions can be measured and integrated with content similarity for topic derivation remains a challenge. In previous work, the strength of tweet-to-tweet relationship has been computed by simply adding measures for content similarity, mentions, and reply-retweets. This simple linear addition does not accurately reflect the various impacts these factors have on tweet relationships. In order to address this issue, we propose a joint probability model that can effectively integrate the effects of the content similarity, mentions, and reply-retweets to measure the tweet relationship for the purpose of topic derivation. The proposed method is based on matrix factorization techniques, which enables a flexible implementation on a distributed system in an incremental manner. Experimental results show that the proposed model results in a significant improvement in the quality of topic derivation over existing methods.

Original languageEnglish
Title of host publicationICDCS 2017
Subtitle of host publicationProceedings of the IEEE 37th International Conference on Distributed Computing Systems
EditorsKisung Lee, Ling Liu
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages2338-2343
Number of pages6
ISBN (Electronic)9781538617922
ISBN (Print)9781538617939
DOIs
Publication statusPublished - 13 Jul 2017
Event37th IEEE International Conference on Distributed Computing Systems, ICDCS 2017 - Atlanta, United States
Duration: 5 Jun 20178 Jun 2017

Other

Other37th IEEE International Conference on Distributed Computing Systems, ICDCS 2017
CountryUnited States
CityAtlanta
Period5/06/178/06/17

Keywords

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

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