About the paper
| Title |
Facebook profiles clustering |
| Book |
ICIST 2016 Proceedings |
| Pages |
154-158 |
| Abstract: Internet social networks may be an abundant
source of opportunities giving space to the “parallel world”
which can and, in many ways, does surpass the realty. People
share data about almost every aspect of their lives, starting
with giving opinions and comments on global problems and
events, friends tagging at locations up to the point of
multimedia personalized content. Therefore, decentralized
mini-campaigns about educational, cultural, political and
sports novelties could be conducted. In this paper we have
applied clustering algorithm to social network profiles with
the aim of obtaining separate groups of people with different
opinions about political views and parties. For network case,
where some centroids are interconnected, we have
implemented edge constraints into classical k-means
algorithm. This approach enables fast and effective
information analysis about the present state of affairs, but
also discovers new tendencies in observed political sphere. All
profile data, friendships, fanpage likes and statuses with
interactions are collected by already developed software for
neurolinguistics social network analysis - “Symbols”. |
Full citation
Arsić, B.,
Bašić, M.,
Spalević, P.,
Ilić, M.,
Veinović, M.
Facebook profiles clustering. In:
Konjović, Z.,
Zdravković, M.,
Trajanović, M.
(Eds.)
ICIST 2016 Proceedings, pp.154-158, 2016
Keywords
Data analytics
Social networks