MINIMIZING INFLUENCE OF RUMOURS ON SOCIAL NETWORKS USING MACHINE LEARNING ALGORITHMS AND ANALYSIS

Authors:

T. C. Subash Ponraj,S. S. Subashka Ramesh,

DOI NO:

https://doi.org/10.26782/jmcms.2020.05.00016

Keywords:

Rumour,malicious,Hybrid SVM,Naive Bayes,KNN,

Abstract

The advancement of large scale online social networks, online data sharing is turning out to be pervasive consistently. Both positive and negative information is spreading through online social networks. It centres on the negative data issues, for example, online rumours. Blocking of online rumour is one of the major issues in large scale social media networks. Hostile rumours can lead to confusion in the public eye and consequently should be quickly as fast as time permits in the wake of being distinguished. For this we used hybrid SVM, Naive Bayes and KNN algorithm. We will probably limit the impact of the rumour which is the quantity of clients that have acknowledged and sent the rumour by obstructing a specific subset of hubs

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