Survey on Efficient Classifier for Detecting Spam in Social Networks
Vishalakshi N S, S.Sridevi
P.G. Student, Department of Computer Science, New Horizon College Of Engineering, Bangalore, Karnataka, India
Assistant Professor, Department of Computer Science, New Horizon College Of Engineering, Bangalore,India
ABSTRACT:Social networking services are used for communication between people to share information through internet.Reaching hundreds millions of users, major social networks have become important target media for spammers. Social networks provide communication between people to share information through internet. The unbounded growth of content and users pushes the Internet technologies usage to certain limitations. The main objective of the proposed work is to find relationship between features and classifying patterns for detecting spam message from the unwanted sites. .In this paper we have reviewed the existing techniques for detecting spam users in social network. Features for the detection of spammers could be user based or content based or both and spam classifier methods.
KEY WORDS: Classification, Data Mining, Machine Learning, Predictive analysis, Social Networking Spam, Spam detection.
I.INTRODUCTION
Within the past few years, online social network, such as Face-book, Twitter, Weibo, etc., has become one of the major way for internet users to keep communications with their friends. According to Statist report [1], the number of social network users has reached 1.61 billion until late 2013, and is estimated to be around 2.33 billion users globe, until the end of 2017. However, along with great technical and commercial success, social network platform also provides a large amount of opportunities for broadcasting spammers, which spreads malicious messages and behaviour. According to Nexgate's report [2], during the first half of 2013, the growth of social spam has been 355%, much faster than the growth rate of accounts and messages on most branded social networks.
The impact of social spam is already significant. A social spam message is potentially seen by all the followers and recipients' friends. Even worse, it might cause misdirection and misunderstand-ing in public and trending topic discussions. For example, trending topics are always abused by spammers to publish comments with URLs, misdirecting all kinds of users to completely unrelated web-sites.
Because most social networks provide shorten service on URLs inside messages it is difficult to identify the content without visiting the site.
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