Social Networking
Volume 2, Issue 4 (October 2013)
ISSN Print: 2169-3285 ISSN Online: 2169-3323
Google-based Impact Factor: 1.07 Citations
Recommending Who to Follow on Twitter Based on Tweet Contents and Social Connections ()
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ABSTRACT
In this paper, we examine methods that can provide accurate results in a form of a recommender system within a social networking framework. The social networking site of choice is Twitter, due to its interesting social graph connections and content characteristics. We built a recommender system which recommends potential users to follow by analyzing their tweets using the CRM114 regex engine as a basis for content classification. The evaluation of the recommender system was based on a dataset generated from real Twitter users created in late 2009.
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