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All societies have been much more bipolar over the past few years,particularly after the emergence of online social networks and media. In fact,the gap between the two ends of social spectrum is going to be even deeperafter the spread of new media. In this circumstance, social polarization hasbeen a growing concern among socialists and computer science experts because ofthe detrimental impact which online social networks can have on societies byadding fuel to the fire of extremism. Several types of research were conductedfor proposing measures to calculate the controversy level in social networks,afterward, to reduce controversy among contradicting viewpoints, for example,by exposing opinions of one side to other side's members. Most of the attemptsfor quantifying social networks' controversy have considered the networks intheir most primary forms, without any attributes. Although these kinds ofresearches provide platform-free algorithms to be used in different socialnetworks, they are not able to take into account a great deal of usefulinformation provided by users (node attributes).To surmount this shortcoming,we propose a framework to be utilized in different networks with differentattributes. We propelled some Biased Random Walks (BRW) to find their path fromstart point to an initially unknown endpoint with respect to initial energy ofstart node and energy loss of nodes on the path. We extracted structuralattribute of networks, using node2vec, and compared it with state-of-the-artalgorithms, and showed its accuracy. Then, we extracted some content attributesof user and analyze their effects on the results of our algorithm. BRW iscompared with another state-of-the-art controversy measuring algorithm. Then,its changes in different level of controversy in Persian Twitter is consideredto show how it works in different circumstance.

A Framework for Quantifying Controversy of Social Network Debates Using Attributed Networks: Biased Random Walk (BRW)