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.