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J. Chen, R. Nairn, L. Nelson, M. Bernstein, and E. H. Chi, “Short and Tweet: Experiments on Recommending Content from Information Streams,” Proceedings of CHI, 2010, pp. 1185-1194. http://www-users.cs.umn.edu/echi/papers/2010CHI/Zerozero88-tweet-recommender-ASC-PARC.pdf

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.