Recommending related microblogs

Lin LI, Huifan XIAO, Guandong XU

Research output: Chapter in Book/Report/Conference proceedingChapters

7 Citations (Scopus)


Computing similarity between short microblogs is an important step in microblog recommendation. In this chapter, the authors utilize three kinds of approaches-traditional term-based approach, WordNetbased semantic approach, and topic-based approach-to compute similarities between micro-blogs and recommend top related ones to users. They conduct experimental study on the effectiveness of the three approaches in terms of precision. The results show that WordNet-based semantic similarity approach has a relatively higher precision than that of the traditional term-based approach, and the topic-based approach works poorest with 548 tweets as the dataset. In addition, the authors calculated the Kendall tau distance between two lists generated by any two approaches from WordNet, term, and topic approaches. Its average of all the 548 pair lists tells us the WordNet-based and term-based approach have generally high agreement in the ranking of related tweets, while the topic-based approach has a relatively high disaccord in the ranking of related tweets with the WordNet-based approach. Copyright © 2013 IGI Global.

Original languageEnglish
Title of host publicationSocial media mining and social network analysis: Emerging research
EditorsGuandong XU, Lin LI
Place of PublicationHershey, PA
PublisherInformation Science Reference
ISBN (Electronic)9781466628076
ISBN (Print)9781466628069
Publication statusPublished - 2013


Li, L., Xiao, H., & Xu, G. (2013). Recommending related microblogs. In G. Xu & L. Li (Eds.), Social media mining and social network analysis: Emerging research (pp. 202-210). Information Science Reference.


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