Community Spam Detection Methodologies for Recommending Nodes
J. Jeyasudha1, G.Usha2
1J. Jeyasudha, Department of Software Engineering, SRM Institute of Science & Technology, Kancheepuram (Tamil Nadu), India.
2Dr. G. Usha, Department of Software Engineering, SRM Institute of Science & Technology, Kancheepuram (Tamil Nadu), India.
Manuscript received on 02 July 2019 | Revised Manuscript received on 12 August 2019 | Manuscript Published on 27 August 2019 | PP: 131-142 | Volume-8 Issue-2S4 July 2019 | Retrieval Number: B10240782S419/2019©BEIESP | DOI: 10.35940/ijrte.B1024.0782S419
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The most popular and leading social network service online now days is Facebook, twitter and Linked In. When socializing becomes usual, the probability of threats and unwanted posts (Spams) comes naturally. To identify and block such Spams, there are a few techniques available recently. However, the efficiency of such tools to combat with spammers seem tedious due to the public unavailability of critical pieces of Facebook Information like Profile, Network Information, Posts and more. Literature shows that there are many researches been carried out to find and combat malicious accounts and spammers over last two decades. In this paper, a review of similar methods that works with detection of spammers in a community on Social Networking Website with the help of mindmap that is given. The work is comprehended in how data is collected, types of spammers, classifiers, machine learning, review on spammers and community detection and whether it is graph based or non graph based dataset. A survey of research publications on Spammers and Malicious account based on malicious categories for the detected communities with the help of various categories discussed in the mindmap.
Keywords: Social Spam, Community Detection, Influential Node.
Scope of the Article: Software Engineering Methodologies