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has been cited by the following article:

• TITLE: Recommending Who to Follow on Twitter Based on Tweet Contents and Social Connections

  AUTHORS: Evgenia Tsourougianni, Nicholas Ampazis

  KEYWORDS: Recommender Systems; Social Networks; Personalization

  JOURNAL NAME: Social Networking, Vol.2 No.4, October 30, 2013

  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.