Detecting Opinion Polarisation on Twitter by Constructing Pseudo-Bimodal Networks of Mentions and Retweets

Zakhlebin, Igor; Semenov, Aleksandr M.; Tolmach, Alexander; Nikolenko, Sergey I.

doi:10.1007/978-3-319-41718-9_10

Cited by 3 publications

(3 citation statements)

References 16 publications

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“…Therefore, when scholars refer to "opinion polarization" in the context of social media, it usually emphasizes the ending state instead of the whole process of opinion polarization. Pursuing this idea, Zakhlebin et al define [62] "opinion polarization" on social media as a state wherein individuals are separated into sides that have little or no communication with and understanding of each other. Lee & Choi [63], on the other hand, define the concept as a state wherein individuals are showing little tolerance toward different opinions, and they are demonstrating increased favoritism towards in-group members and their own ideas.…”

Section: How To Detect and Measure Opinion Polarization In Social Media?mentioning

confidence: 99%

“…It is easy to put into practice, but only works when the keywords or hashtags express tendentious connotations clearly. Zakhlebin et al indicate that opinion polarization can be legibly observed on unimodal projections of artificially created bimodal networks [62]. Based on the findings, they then propose an approach using a pseudobimodal model to detect and explore opinion polarization in Twitter communication networks.…”

Section: How To Detect and Measure Opinion Polarization In Social Media?mentioning

confidence: 99%

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Studying opinion polarization on social media

Jiang

2022

Soc Work Soc Welf

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Opinion polarization on social media raises a lot of concerns today. In this study, the author provides a systematic review of publications about the issues since 2013 to show the achievements in the existing research on the topic, to sort out the relevant knowledge, and to provide some inspirations for future research in this area. This paper finds that opinion polarization on social media is initiated by three patterns of factors: increasing the homophily in discussions, increasing conflict in social media discussions, and facilitating the spread of misinformation. It also summarizes the existing findings on how to detect and measure opinion polarization in social media, and comes up with opportunities for further researches on this topic.

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Section: How To Detect and Measure Opinion Polarization In Social Media?mentioning

confidence: 99%

Section: How To Detect and Measure Opinion Polarization In Social Media?mentioning

confidence: 99%

Studying opinion polarization on social media

Jiang

2022

Soc Work Soc Welf

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“…There are different techniques called projections to transform two-mode graphs to their one-mode versions [57,67]. Sometimes, researchers even do transformations in backward direction to consider interactions between different subgroups of actors as they were from different modes of the corresponding two-mode network [18,91].…”

Section: -Mode Network As Two-mode Onesmentioning

confidence: 99%

Multimodal Clustering for Community Detection

Ignatov

Semenov

Komissarova

et al. 2017

Formal Concept Analysis of Social Networks

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Multimodal clustering is an unsupervised technique for mining interesting patterns in n-adic binary relations or n-mode networks. Among different types of such generalized patterns one can find biclusters and formal concepts (maximal bicliques) for 2-mode case, triclusters and triconcepts for 3-mode case, closed nsets for n-mode case, etc. Object-attribute biclustering (OA-biclustering) for mining large binary datatables (formal contexts or 2-mode networks) arose by the end of the last decade due to intractability of computation problems related to formal concepts; this type of patterns was proposed as a meaningful and scalable approximation of formal concepts. In this paper, our aim is to present recent advance in OAbiclustering and its extensions to mining multi-mode communities in SNA setting. We also discuss connection between clustering coefficients known in SNA community for 1-mode and 2-mode networks and OA-bicluster density, the main quality measure of an OA-bicluster. Our experiments with 2-, 3-, and 4-mode large realworld networks show that this type of patterns is suitable for community detection in multi-mode cases within reasonable time even though the number of corresponding n-cliques is still unknown due to computation difficulties. An interpretation of OA-biclusters for 1-mode networks is provided as well.

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