Abstract
Social networks can be a very successful tool to engage users to discuss relevant topics for society. However, there are also some dangers that are associated with them, such as the emergence of polarization in online discussions. Recently, there has been a growing interest to try to understand this phenomenon, as some consider that this can be harmful concerning the building of a healthy society in which citizens get used to polite discussions and even listening to opinions that may be different from theirs. In this work, we face the problem of defining a precise measure that can quantify in a meaningful way the level of polarization present in an online discussion. We focus on the Reddit social network, given that its primary focus is to foster discussions, in contrast to other social networks that have some other uses. Our measure is based on two different characteristics of an online discussion: the existence of a balanced bipartition of the users of the discussion, where one partition contains mainly users in agreement (regarding the topic of the discussion) and the other users in disagreement, and the degree of negativity of the sentiment of the interactions between these two groups of users. We discuss how different characteristics of the discussions affect the value of our polarization measure, and we finally perform an empirical evaluation over different sets of Reddit discussions about diverse classes of topics. Our results seem to indicate that our measure can capture differences in the polarization level of different discussions, which can be further understood when analyzing the values of the different factors used to define the measure.
Highlights
Nowadays, there is growing controversy regarding the emergence of polarization in discussions on social networks, and the responsibility of companies in this problem.For example, Facebook researchers have studied the spread of divisive content on the platform [1,2]
Our main focus in this work is to give a more clear and quantitative model for measuring polarization in an online debate such that this behavior can be monitored for generating a warning signal when necessary, that is, to detect communication patterns where users seem to interact positively only with a fixed group of users and negatively with the rest
We introduce a quantitative model for measuring polarization in an online debate such that this behavior can be monitored for generating a warning signal when the debate polarization reaches some threshold value
Summary
There is growing controversy regarding the emergence of polarization in discussions on social networks, and the responsibility of companies in this problem. The findings of Facebook suggest that completely fixing the polarization problem may be difficult As they commented in an internal company presentation [4], Facebook algorithms exploit the human brain’s attraction to divisiveness and tend to feed users with more and more divisive content, which seems to ensure that users will spend more time on the platform. Our main focus in this work is to give a more clear and quantitative model for measuring polarization in an online debate such that this behavior can be monitored for generating a warning signal when necessary, that is, to detect communication patterns where users seem to interact positively only with a fixed group of users and negatively with the rest.
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