Spam, a Digital Pollution and Ways to Eradicate It
Chinthapanti Bharath Sai Reddy1, Shaurya Chaudhary2, Saravana Kumar Kandasamy3
1Chinthapanti Bharath Sai Reddy, Department of CSE, Vellore Institute of Technology, Vellore (Tamil Nadu) India.
2Shaurya Chaudhary, Department of CSE, Vellore Institute of Technology, Vellore (Tamil Nadu) India.
3Saravanakumar Kandasamy, Department of Computer Science, Vellore Institute of Technology, Vellore (Tamil Nadu) India.
Manuscript received on November 22, 2019. | Revised Manuscript received on December 15, 2019. | Manuscript published on December 30, 2019. | PP: 2630-2635 | Volume-9 Issue-2, December, 2019. | Retrieval Number: B4107129219/2019©BEIESP| DOI: 10.35940/ijeat.B4107.129219
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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: Due to the growing popularity of the microblogging and networking sites like twitter, Gmail, Facebook etc., there has been an increase in the number of spammers. Spammers on Twitter seem to be more dangerous than the mail spammers as they exploit the limitation on the characters of Twitter for their own purposes. Spammers have also become creative in framing their content to cleverly escape the classifiers. This survey is thus mainly used to discuss and analyze the recent research that had been put forth regarding the spam detection in social media sites such as Twitter. This survey analyses the papers that tackled various problems faced on Twitter and the problems faced by the methods that have already been presented before. We then compared all the methods present in the papers to see which method or combination of methods could give the best result in detecting spam.
Keywords: Bayes methods, Classification algorithms, Clustering algorithms, Feature extraction and Machine learning algorithms