A Framework for Quantifying Controversy of Social Network Debates Using Attributed Networks: Biased Random Walk (BRW)
Hanif Emamgholizadeh, Milad Nourizade, Mir Saman Tajbakhsh, Mahdieh, Hashminezhad, Farzaneh Nasr Esfahani

TL;DR
This paper introduces a novel framework using Biased Random Walks on attributed social networks to quantify controversy levels, incorporating user attributes for more accurate analysis of social polarization.
Contribution
It proposes a new attributed network controversy measurement framework utilizing Biased Random Walks and compares its effectiveness with existing algorithms.
Findings
The framework accurately measures controversy in attributed social networks.
Node2vec effectively captures network structural attributes.
The method successfully analyzes controversy levels in Persian Twitter.
Abstract
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 deeper after the spread of new media. In this circumstance, social polarization has been a growing concern among socialists and computer science experts because of the detrimental impact which online social networks can have on societies by adding fuel to the fire of extremism. Several types of research were conducted for 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 attempts for quantifying social networks' controversy have considered the networks in their most primary forms, without any attributes.…
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