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


Ontology type: schema:Chapter     


Chapter Info

DATE

2016

AUTHORS

Igor Zakhlebin , Aleksandr Semenov , Alexander Tolmach , Sergey Nikolenko

ABSTRACT

We present a novel approach to analyze and visualize opinion polarisation on Twitter based on graph features of communication networks extracted from tweets. We show that opinion polarisation can be legibly observed on unimodal projections of artificially created bimodal networks, where the most popular users in retweet and mention networks are considered nodes of the second mode. For this purpose, we select a subset of top users based on their PageRank values and assign them to be the second mode in our networks, thus called pseudo-bimodal. After projecting them onto the set of “bottom” users and vice versa, we get unimodal networks with more distinct clusters and visually coherent community separation. We developed our approach on a dataset gathered during the Russian protest meetings on 24th of December, 2011 and tested it on another dataset by Conover [13] used to analyze political polarisation, showing that our approach not only works well on our data but also improves the results from previous research on that phenomena. More... »

PAGES

169-178

References to SciGraph publications

  • 2011-12. The Dynamics of Protest Recruitment through an Online Network in SCIENTIFIC REPORTS
  • 2012. Gaining Insight in Social Networks with Biclustering and Triclustering in PERSPECTIVES IN BUSINESS INFORMATICS RESEARCH
  • Book

    TITLE

    Information Retrieval

    ISBN

    978-3-319-41717-2
    978-3-319-41718-9

    From Grant

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-3-319-41718-9_10

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    http://dx.doi.org/10.1007/978-3-319-41718-9_10

    DIMENSIONS

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