A Study of Spam Detection Algorithm on Social Media Networks
In the present situation, the issue of identifying spammers has received increasing attention because of its practical relevance in the field of social network analysis. The growing popularity of social networking sites has made them prime targets for spa
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Abstract In the present situation, the issue of identifying spammers has received increasing attention because of its practical relevance in the field of social network analysis. The growing popularity of social networking sites has made them prime targets for spammers. By allowing users to publicize and share their independently generated content, online social networks become susceptible to different types of malicious and opportunistic user actions. Social network community users are fed with irrelevant information while surfing, due to spammer’s activity. Spam pervades any information system such as email or Web, social, blog, or reviews platform. Therefore, this paper attempts to review various spam detection frameworks that which deal about the detection and elimination of spams in various sources. Keywords Spam detection
Spam analysis Feature extraction
1 Introduction Social networks such as Facebook, MySpace, LinkedIn, Friendster [1], and Tickle have millions of members who use them for both social and business networking. Due to the astonishing amount of information on Web, users follow the way of searching useful Web pages by querying search engines. Given a query, a search engine identifies the relevant pages on the Web and presents the users with the links to such pages. Spammers try to increase the page rank of the target Web page in search results by search engine optimization (SEO), the injection of artificially created pages into the Web in order to influence the results from search engines to
J. S. Saini (&) Department of Computer Science and Engineering, Sree Narayana Gurukulam College of Engineering, Kadayiruppu, Kolenchery, Kerala, India e-mail: [email protected]
G. S. S. Krishnan et al. (eds.), Computational Intelligence, Cyber Security and Computational Models, Advances in Intelligent Systems and Computing 246, DOI: 10.1007/978-81-322-1680-3_22, Springer India 2014
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drive traffic to certain pages for fun or profit. Initially, spams are introduced in mails. Later, this has been extended to social networks. On the other hand, in email system, spammer sends unsolicited bulk email to users by redirecting them to irrelevant Websites. The success of delivered attacks is dependent almost entirely upon the click-through rate of the email. If the target does not click on the malicious link presented in the email, then the attack usually fails. To improve click-through rates, many techniques exist such as hiding the destination of hyperlinks, falsifying header information, and creative use of images [2, 3], etc. Email messages also take advantage of some shared context among friends on a social network such as celebrations of birthday functions, residing in the same home town, or common events participation. This shared context dramatically increase email authenticity, filters and increasing the click-through rate for spam that contains advertisements, installs malicious software, or solicits sensitive personal information [4, 5], etc. But, in the content of blog p
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