Profiling Online Social Networks for Spam Detection

Authors(2) :-K. Srinivasan, V. Sureka

Social network has become a very general way for internet users to connect and interact online. Users spend sufficiently of time on famous social networks (e.g., Facebook, Twitter, Sina Weibo, etc.), reading news, discussing events and posting messages. Unfortunately, this popularity also attracts a significant amount of spammers who continuously expose malicious behaviour (e.g., post messages containing commercial URLs, following a larger amount of users, etc.), foremost to great misinterpretation and inconvenience on users' social activities. In this paper, a supervised machine learning based solution is proposed for an effective spam detection.

Authors and Affiliations

K. Srinivasan
M.Phil Research Scholar, Department of Computer Science, Sri Jayendra Saraswathy Maha Vidyalaya CAS, Coimbatore, Tamil Nadu, India
V. Sureka
Assistant Professor, Department of Computer Science, Sri Jayendra Saraswathy Maha Vidyalaya CAS, Coimbatore, Tamil Nadu, India

OSN, k-NN classifier, Spam detection

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Publication Details

Published in : Volume 2 | Issue 5 | September-October 2017
Date of Publication : 2017-09-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 488-491
Manuscript Number : CSEIT1725107
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

K. Srinivasan, V. Sureka, "Profiling Online Social Networks for Spam Detection", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 2, Issue 5, pp.488-491, September-October-2017.
Journal URL : http://ijsrcseit.com/CSEIT1725107

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