Separating Polarization from Noise: Comparison and Normalization of Structural Polarization Measures

Abstract

Quantifying the amount of polarization is crucial for understanding and studying political polarization in political and social systems. Several methods are used commonly to measure polarization in social networks by purely inspecting their structure. We analyse eight of such methods and show that all of them yield high polarization scores even for random networks with similar density and degree distributions to typical real-world networks. Further, some of the methods are sensitive to degree distributions and relative sizes of the polarized groups. We propose normalization to the existing scores and a minimal set of tests that a score should pass in order for it to be suitable for separating polarized networks from random noise. The performance of the scores increased by 38%-220% after normalization in a classification task of 203 networks. Further, we find that the choice of method is not as important as normalization, after which most of the methods have better performance than the best-performing method before normalization. This work opens up the possibility to critically assess and compare the features and performance of different methods for measuring structural polarization.

The Russian invasion of Ukraine selectively depolarized the Finnish NATO discussion on Twitter

“…1C). This observation is confirmed by the number of external retweets of the pro group, the number of internal retweets, and the E/I ratio in each anti group (Table 1): although the E/I ratio of the conspiracy-anti group also more than doubled in the first week after the invasion (some of this change might be explained by overfitting [45]), the E/I ratio of the left-anti group had an almost tenfold increase in the same period. This structural change in the retweet network also remains in the 1-week-after and 4-weeks-after periods (see Appendix for a visualization of the retweet networks in these two periods).…”

Affective and interactional polarization align across countries

“…Since our aim is to identify politicians who are ideologically aligned our polarization analysis uses retweet networks, a common approach in many Twitter-based polarization studies [13,27,33]. We focus on retweets since they are generally evidence of a Twitter user endorsing the message of the original poster [52], as opposed to other Twitter interactions (mentions, quotes or replies) which may indicate a positive, negative or neutral relationship between the two users.…”

“…However, the rise of the internet has seen social media emerge as an alternate public for the study of polarization [10,11]. On social media, the most commonly studied form of po-larization is interactional polarization [12] -sometimes referred to as structural [13] or social network polarization [14] -which looks at how the interaction patterns between ideological groups are segregated.…”