Method for measuring Twitter content influence

Euijong Lee, Jeong Dong Kim, Doo Kwon Baik

Research output: Contribution to journalConference articlepeer-review

2 Citations (Scopus)


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
Pages (from-to)659-664
Number of pages6
JournalProceedings of the International Conference on Software Engineering and Knowledge Engineering, SEKE
Issue numberJanuary
Publication statusPublished - 2014
Event26th International Conference on Software Engineering and Knowledge Engineering, SEKE 2014 - Vancouver, Canada
Duration: 2014 Jul 12014 Jul 3


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

ASJC Scopus subject areas

  • Software


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