The paper tackles the problem of predicting which users out of a set of recommended users will be followed within a micro-blogging system, considering content, structural properties of the followergraph and visibility of user content. Various metrics for capturing these properties are introduced and in the experimental setup used to train a classifier (logistic regression) for predicting recommended users.
The paper's introduction, motivation and relevant work section is very well written and thus it is a good starting point for diving into the literature of link prediction in the context of micro-blogging systems.
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%0 Journal Article
%1 rowe2012follow
%A Rowe, M.
%A Stankovic, M.
%A Alani, H.
%D 2012
%K
%T Who will follow whom? Exploiting Semantics for Link Prediction in Attention-Information Networks
%U http://scholar.google.com/scholar.bib?q=info:5c2M6COZfLcJ:scholar.google.com/&output=citation&hl=de&as_sdt=0,5&ct=citation&cd=0
@article{rowe2012follow,
added-at = {2012-08-27T13:17:50.000+0200},
author = {Rowe, M. and Stankovic, M. and Alani, H.},
biburl = {https://www.bibsonomy.org/bibtex/210138c263719492e6ad0db9a3981185b/folke},
interhash = {c699f3859d03901a07e003fd37cf281b},
intrahash = {10138c263719492e6ad0db9a3981185b},
keywords = {},
timestamp = {2012-08-27T13:17:51.000+0200},
title = {Who will follow whom? Exploiting Semantics for Link Prediction in Attention-Information Networks},
url = {http://scholar.google.com/scholar.bib?q=info:5c2M6COZfLcJ:scholar.google.com/&output=citation&hl=de&as_sdt=0,5&ct=citation&cd=0},
year = 2012
}