A framework for analyzing and detracting negative emotional contagion in online social networks

Abstract

Online social networks are being a powerful platform for the spread of negative emotion contagion which is affecting users from different perspectives i.e. psychology, economics, marketing and neuroscience. Online social networks have huge amount of data and knowledge that need to be studied through the use of data mining techniques. This paper focuses on presenting a new framework for analyzing and detracting negative emotional contagion through the use of clustering for detecting the community where the negative emotions may spread. Also, classification of nodes in the network is used to analyze the negativity in the nodes to help decide on the best treatment. Moreover, Prominent Actors (PAs) in the network must be determined to help in the treatment. Finally, through the use of recommender system and positive contagion the best treatment will be introduced. This framework is important to find the negative emotional contagion in different online communities and try to cure it or immunize the community against it.