Semi-Supervised Clustering Algorithm for Rumor Minimization and Propagation with Classification in Social Networks

Social networks permit speedy disperse of innovations and notions despite the fact that rumor or negative information can also spread extensively. Bearing in mind, about its significance and impact, in addition to its complexity, much of the attention of researchers has been pulled by rumor. Theme of online social networks like twitter circumstances is differing from conventional websites social network is considered in this work. A model of dynamic rumor influence reduction with classification (MDRIPC) is projected in this work. Major objective of this paper is to reduce the impact of the rumor from the dataset which is actually amount of users admitted and sent rumor. This is achieved using the classifiers such as Naive Bayes, Random forest and KNN by blocking an exact set of nodes with imbalanced dataset. But some records belonging to same group are considerably in huge number and some are very uncommon and hence several datasets are imbalanced. Also the performance of a classifier is influenced significantly by this imbalanced nature of the datasets. Random sampling technique is applied to handle with this issue. Semi Supervised Clustering Algorithm (SSCA) is employed furthermore, to solve rumor propagation issue and this is implemented through the information gathered by the analysis of social networks. Supported large-scale world networks are utilized for the experiments and the effectiveness of the three classifiers are validated.