Learning to Find Topic Experts in Twitter via Different Relations

Expert finding has become a hot topic along with the flourishing of social networks, such as micro-blogging services like Twitter. Finding experts in
Twitter
is an important problem because tweets from experts are valuable sources that carry rich information (e.g., trends) in various domains. However, previous methods cannot be directly applied to
Twitter
expert finding problem. Recently, several attempts use the relations among users and
Twitter List
s for expert finding. Nevertheless, these approaches only partially utilize such relations. To this end, we develop a probabilistic method to jointly exploit three types of relations (i.e.,
follower
relation,
user-list
relation, and
list-list
relation) for finding experts. Specifically, we propose a
S
emi-
S
upervised
G
raph-based
R
anking approach (
$\sf{SSGR}$
) to offline calculate the
global authority
of users. In
$\sf{SSGR}$
, we employ a normalized Laplacian regularization term to jointly explore the three relations, which is subject to the supervised information derived from Twitter crowds. We then online compute the
local relevance
between users and the given query. By leveraging the
global authority
and
local relevance
of users, we rank all of users and find top-N users with highest ranking scores. Experiments on real-world data demonstrate the effectiveness of our proposed approach for
topic-specific
expert finding in
Twitter
.