Spam Detection Framework using ML Algorithm
Vinodhini. M1, Prithvi. D2, Balaji. S3
1Vinodhini. M, Department of Computer Science and Engineering, SRM Institute of Science and Technology, Chennai, Tamil Nadu, India.
2Prithvi. D*, Department of Computer Science and Engineering, SRM Institute of Science and Technology, Chennai, India.
3Balaji. S, Department of Computer Science and Engineering, SRM Institute of Science and Technology, Chennai, India.
Manuscript received on February 28, 2020. | Revised Manuscript received on March 22, 2020. | Manuscript published on March 30, 2020. | PP: 5326-5329 | Volume-8 Issue-6, March 2020. | Retrieval Number: F1120038620/2020©BEIESP | DOI: 10.35940/ijrte.F1120.038620
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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 current use of social media has created incomparable amounts of social data, as it is a cheap and popular information sharing communication platform. Nowadays, a huge percentage of people depend on the accessible material on social networking in their choices (e.g. comments and suggestions about a subject or product). This feature on exchanging knowledge with a wide number of users has quickly prompted social spammers to exploit the network of confidence to distribute spam messages and support personal forums, advertising, phishing, scams and so on. Identifying these spammers and spam material is a hot subject of study, and while large amounts of experiments have recently been conducted to this end, so far the methodologies are only barely able to identify spam feedback, and none of them demonstrates the value of each derived function type. In this study, we have suggested a machine learning-based spam detection system that determines whether or not a specific message in the dataset is spam using a set of machine learning algorithms. Four main features have been used; including user-behavioral, user-linguistic, review-behavioral and review-linguistic, to improve the spam detection process and to gather reliable data.
Keywords: Spam Detection, Machine Learning, Random Forest algorithm, Reviews, Framework, Social Media
Scope of the Article: Data Management.