A Multi-Opinion Primarily Based Metric For Quantifying Polarization On Social Networks: A Case Research From India

News2021) or COVID-19 outbreak Ridhwan and Hargreaves (2021), appraising moral foundations Kaur and Sasahara (2016), linking the public opinion obtained from various surveys with the Twitter information O’Connor et al. 2011), predicting movie revenues Asur and Huberman (2010) and predicting the outcomes of various elections Tumasjan et al. 2010), political campaigning Aragón et al. 2013); LaMarre and Suzuki-Lambrecht (2013); Enli and Skogerbø (2013), learning voter participation during elections Bekafigo and McBride (2013), selling social movements Borge-Holthoefer et al.

It contains the benchmark networks, various artificial networks and completely different traditional social networks of authors of our collected tweets. Finally, we elaborated our method of quantifying polarization in a hybrid situation i.e. not simply looking at communications, but moderately focusing on communication among customers with respect to their social relationships. To check the mass views on the applied act, we leverage the Twitter Streaming API for collecting tweets utilizing key phrases CAB111CAB-Citizenship Amendment Invoice, NRC222NRC-National Register for Residents, CAA and CitizenshipAct. Hasan et al. (2014); Zhu et al. Initially, all of the unique hashtags present in the tweets are extracted.

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2022). Equally, there may be multiple neighborhood belonging to a bunch of individuals having an identical opinion, due to the existence of giant number of communities in a network Labatut et al. 2014). The customers current inside a group in any complicated community have frequent communications (or robust connections) amongst them Girvan and Newman (2002). Therefore, it turns into vital to quantify polarization with respect to communication between customers with different opinions belonging to related or different communities, where an opinion need not to be binary in nature. To the better of our information, there has not been any metric proposed in literature which can be used to offer a polarization score for the given network with multi-opinion state of affairs.

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On the contrary, the point out based communications among the users were not polarized in nature. We reside in a society aided by the tools of fashionable communication equivalent to Twitter, Fb and so on. The micro-blogging platform Twitter which was based in 2006 has come a long way with over 330 million customers worldwide Krings (2019). There have been numerous – image source – research prior to now which discover the platform for quite a lot of topics including analysing vaccine hesitancy Thelwall et al.

EUFor quantifying polarization amongst users engaged in CAA discussions on Twitter, we applied our proposed metric on variety of social networks. . Thus, we goal to quantify the extent of polarization in the communications on a controversial situation concerning buddies (or strangers). Based mostly on this relationship categorization, consider the next analysis question: Do we retweet/point out our buddies (or strangers) based on opinion similarity on a controversial difficulty? A social relationship network (obtained using follower-followee connections). Apart from these traditional social networks, we also measured polarization on hybrid networks, which are the mix of communication networks and social relationship community. These hybrid networks would help us in quantifying polarization with respect to two kinds of users. This contains the standard social networks reminiscent of communication networks (obtained utilizing retweets or mentions).

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