Recommending related microblogs

Li, L; Xiao, H; Xu, G

Recommending related microblogs

Li, L Xiao, H Xu, G

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Publication Type:: Chapter
Citation:: Social Media Mining and Social Network Analysis: Emerging Research, 2013, pp. 202 - 210
Issue Date:: 2013-12-01

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Field	Value	Language
dc.contributor.author	Li, L	en_US
dc.contributor.author	Xiao, H	en_US
dc.contributor.author	Xu, G https://orcid.org/0000-0003-4493-6663	en_US
dc.date.issued	2013-12-01	en_US
dc.identifier.citation	Social Media Mining and Social Network Analysis: Emerging Research, 2013, pp. 202 - 210	en_US
dc.identifier.isbn	9781466628069	en_US
dc.identifier.uri	http://hdl.handle.net/10453/26294
dc.description.abstract	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. © 2013, IGI Global.	en_US
dc.relation.ispartof	Social Media Mining and Social Network Analysis: Emerging Research	en_US
dc.relation.isbasedon	10.4018/978-1-4666-2806-9.ch013	en_US
dc.title	Recommending related microblogs	en_US
dc.type	Chapter
utslib.for	080109 Pattern Recognition and Data Mining	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney/Strength - AAI - Advanced Analytics Institute Research Centre
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

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. © 2013, IGI Global.

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http://hdl.handle.net/10453/26294