2013). A dipole second based mostly index for opinion has also been proposed used to measure polarization in Twitter discussions related to the late president of Venezuela Morales et al. Garimella and Weber (2017) measured polarization with respect to hashtags usage, the retweeting pattern in addition to following behaviour of users related to 2 U.S. 2015). Garimella et al. U.S. midterm elections. Various measures for quantifying controversies have been introduced utilizing community properties reminiscent of random walks, graph embeddings, betweenness and boundary connectivity Guerra et al.

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One of many subjects that has garnered fairly an attention among the researchers within the current years is the quantification of opinion polarization using information from Twitter. Varied techniques have been proposed in the literature. Here, the opinion polarization is a social phenomenon specializing in the division among the many users on social media – how you can help – , primarily based on opinions Banisch and Olbrich (2019). Quantifying polarization of opinions is quite challenging. The number of excessive degree customers current on the boundaries of two probably polarized communities can be utilized to quantify the extent of polarization in social networks Guerra et al.

2013); Garimella et al. 2022). Moreover, the second assumption doesn’t consider the fact that a neighborhood could have users with different opinions Singh et al. The existing methodologies, as mentioned, generally consider an opinion to be a binary entity and therefore divides the given community into two communities, assuming that all customers within a community possess related opinion and completely different opinion across the communities. Right here, the first assumption ignores the truth that it is possible that there may very well be more than two opinions towards a given challenge, for instance, a impartial opinion Singh et al.

The strategy proposed in our examine gives a polarization score for a given community by combining the individual scores obtained from two different sets of edges within the community. The first score focuses solely on quantifying polarization by contemplating solely edges connecting nodes belonging to an analogous group. Alternatively, the second rating primarily focuses on quantifying polarization throughout the communities by contemplating solely edges connecting nodes belonging to different communities. The efficiency of the proposed metric has been verified by applying it on completely different benchmark networks in addition to artificial networks with completely different variety of distinct opinions among the many users and having various degree of polarization.