Artikel,
Detecting Linkedin Spammers and its Spam Nets
International Journal of Advanced Computer Science and Applications(IJACSA), (2013)
Zusammenfassung
Spam is one of the main problems of the WWW.
Many studies exist about characterising and detecting several
types of Spam (mainly Web Spam, Email Spam, Forum/Blob
Spam and Social Networking Spam). Nevertheless, to the best of
our knowledge, there are no studies about the detection of Spam
in Linkedin. In this article, we propose a method for detecting
Spammers and Spam nets in the Linkedin social network. As
there are no public or private Linkedin datasets in the state of
the art, we have manually built a dataset of real Linkedin users,
classifying them as Spammers or legitimate users.
The proposed method for detecting Linkedin Spammers
consists of a set of new heuristics and their combinations using
a kNN classifier. Moreover, we proposed a method for detecting
Spam nets (fake companies) in Linkedin, based on the idea that
the profiles of these companies share content similarities. We have
found that the proposed methods were very effective. We achieved
an F-Measure of 0.971 and an AUC close to 1 in the detection
of Spammer profiles, and in the detection of Spam nets, we have
obtained an F-Measure of 1.
