Advanced Machine Learning Technique to Handle Filtering Unwanted Messages in Online Social Networks
B. Sujatha1, K .V.S.S.R Murthy2

1B.Sujatha, CST, S.R.K.R.E.C., Bhimavaram, India.
2K.V.S.S.R.Murthy, CST, S.R.K.R.E.C., Bhimavaram, India. 

Manuscript received on September 16, 2019. | Revised Manuscript received on 24 September, 2019. | Manuscript published on October 10, 2019. | PP: 5374-5377 | Volume-8 Issue-12, October 2019. | Retrieval Number: L37801081219/2019©BEIESP | DOI: 10.35940/ijitee.L3780.1081219
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Abstract: Social networks are increased in present days because of communication between different users like Face book, Google and Twitter. Major fundamental issue behind online social networks is control user’s messages in-front of sharing rumor related messages and posts unwanted messages. It is still main challenge to define user’s share other user’s details in social network communication. In this paper, Greedy Heuristic based Advanced Short Text Classifier (GHASTC) used to classify filtering with rumor related classification for multi user’s interaction in social networks. This hybrid approach gives direct control to users to control unwanted data posted on own space. The proposed approach works with rule based filtering system, which consists a customized filtering for unwanted content in online social networks. The experimental results show efficient filtering results with comparison of traditional techniques.
Keywords: Unwanted Messages, Filtering Data, Classification, Clustering Approach, Online Social Networks.
Scope of the Article: Clustering