Advanced Machine Learning Approach to Handle Filtering Unwanted Messages in Online Social Networks

摘要

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 rumour related messages and posts unwanted messages. It is still challenge to define user's share other user's details in social network communication. So that in this paper, Greedy Hubristic based Advanced Short Text Classifier (CHASTC). To classify filtering with rumour 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. Our proposed approach works with rule based filtering system, which consists a customized filtering for unwanted content in online social networks. Our experimental results show efficient filtering results with comparison of traditional techniques.