Method for measuring Twitter content influence

Euijong Lee, Jeong Dong Kim, Doo Kwon Baik

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

1 Citation (Scopus)

Abstract

Twitter is a microblogging website with specific characteristics not found in other social network services. This platform contains a good deal of valuable content, and users can access this content using Twitter search. However, Twitter search returns only time-descending ordered content including keywords. Thus, we propose a linear-time method of measuring the influence of Twitter content considering not only time, but also characteristics of each Twitter account. In analyzing these characteristics, we have found that the number of retweets can measure shareability, while the number of followers held by the content author can measure spreadability. We perform experiments using real Twitter data for proving the effectiveness of the proposed method. We demonstrate that this proposed method is effective at finding up-to-date content. Further, in comparing our method with analysis via PageRank, we demonstrate that our method is more effective at accurately measuring influence.

Original languageEnglish
Title of host publicationProceedings of the International Conference on Software Engineering and Knowledge Engineering, SEKE
PublisherKnowledge Systems Institute Graduate School
Pages659-664
Number of pages6
Volume2014-January
EditionJanuary
Publication statusPublished - 2014
Event26th International Conference on Software Engineering and Knowledge Engineering, SEKE 2014 - Vancouver, Canada
Duration: 2014 Jul 12014 Jul 3

Other

Other26th International Conference on Software Engineering and Knowledge Engineering, SEKE 2014
CountryCanada
CityVancouver
Period14/7/114/7/3

Keywords

  • Contents search
  • Follower, contents influence
  • Retweet
  • Twitter

ASJC Scopus subject areas

  • Software

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  • Cite this

    Lee, E., Kim, J. D., & Baik, D. K. (2014). Method for measuring Twitter content influence. In Proceedings of the International Conference on Software Engineering and Knowledge Engineering, SEKE (January ed., Vol. 2014-January, pp. 659-664). Knowledge Systems Institute Graduate School.