Proposing a multi-opinion primarily based methodology for quantifying polarization in a social network. It includes the technique of gathering tweets. Testing the efficiency of the proposed methodology in quantifying polarization on binary opinion based mostly benchmark networks as well as a number of synthetic networks with different stage of polarization and having various number of opinions among users. Labeling the authors of the tweets with three totally different opinions. We’ve got then described our proposed multi-opinion based mostly metric for quantification of polarization. CAA, on varied social networks equivalent to communications, social relationships as well as a mixture of each i.e. social relationships primarily based communications. As we have now carried out our proposed model on numerous networks, a quick introduction and the development mechanisms of those networks has been supplied. This part elucidates the varied procedures employed in our study.

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‘irrelevant’ if it is not associated to the given concern, with the help of two annotators having information of Indian politics. CAA hashtags present in the tweets for ground reality labelling., The overall frequency of these hashtags were computed and are shown in Figure 2. For readability purposes, only the twenty most often occurring hashtags of each kinds are proven. This resulted in 97000 users with total 4.1 million tweets. The hashtags like IndiaSupportsCAA, IndiaSupportsCAB, CAASupport, IndiaDoesNotSupportCAA and IndiaRejectsCAB additionally occurred continuously in the tweets. As seen from the figure, the highly used hashtag is IndiaAgainstCAA with a frequency of greater than 0.5 million. Further, to identify user’s opinion, we retained only those customers who posted at the very least 10 tweets related to the given difficulty. The remainder of the hashtags were filtered as they were both ‘irrelevant’ or the annotators disagreed on them.

Citizenship Amendment Act (CAA) in India. We additionally obtained a high polarization score for a social relationships community. For a retweet based mostly hybrid network, the scores returned by our polarization metric highlighted opinion to be the key driver in retweeting behaviour regardless of the social relationships among users. Thus indicating the presence of homophily among users i.e. opinion on CAA is in-line with their social relationships on the platform. We’ve measured the polarization on a wide range of social networks corresponding to communication networks based on retweets or mentions, a social relationships community based on follower-followee connections and finally hybrid networks by combining the communication networks with the social relationships network. The proposed technique prompt a high degree of polarization among the many users with respect to sharing posts on Twitter, thereby indicating the highly contentious nature of the issue.

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We employed our multi-opinion metric for quantifying polarization related to Twitter discussions regarding an Indian problem. 2020), which has the world’s largest democracy bbc (2019a). As an example, we now have used the discussions relating to the implementation of the Citizenship Amendment Act (CAA) by the Indian Authorities in Dec 2019 on the micro-running a blog platform. The key motivation behind specializing in Indian context is the lack of research that makes use of Twitter to check polarization of opinions on issues associated to India Tyagi et al.

Social media has been recognized to be a hotbed of political and social communications and analyzing the polarization of opinions has been gaining consideration. Our metric was examined on both binary opinion based benchmark networks as well as synthetically opinion-labeled social networks with a multi-opinion scenario and varying degrees of polarization. On this examine, we now have proposed a measure for quantifying polarization on social networks. The proposed metric, in contrast to state-of-the-artwork methods, does not assume a two-opinion scenario and applies to a number of opinions. The metric showed promising outcomes for social networks with different levels of polarization.