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‘Opinion tree’: a method for mapping online discussions based on neural-network topic modeling and abstractive summarization

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Svetlana S. Bodrunova

Doctor of Political Sciences, Professor, Department of Media Management and Mass Communications, Institute “Higher School of Journalism and Mass Communications”, Saint Petersburg State University, Saint Petersburg, Russia; ORCID 0000-0003-0740-561X

e-mail: s.bodrunova@spbu.ru
Ivan S. Blekanov

Head of the Department of Programming Technologies, Faculty of Applied Mathematics – Control Processes, St. Petersburg State University, Saint Petersburg, Russia; ORCID 0000-0002-7305-1429

e-mail: I.blekanov@spbu.ru
Nikita A. Tarasov

Junior Research Fellow, Faculty of Applied Mathematics – Control Processes, St. Petersburg State University, Saint Petersburg, Russia; ORCID 0000-0002-9473-6179

e-mail: nkt.tarasov@yandex.ru

Section: Artificial Intelligence in Media and Communication Studies

So far, no neural-network-based methodologies that aim at online opinion detection allow for representing user discussions on social networks in forms that would simultaneously capture cumulation, shift, and dissipation of consensus. Such a method would allow for scrupulous tracing of the opinion dynamics (including polarization of views), shorten the time for evaluation of opinion dynamics, and help address several theoretical assumptions on the nature of cumulative opinions. We propose a method for construction of ‘opinion trees’ within user discussions on social networks. The case dataset features a Reddit discussion of the 27th UN Climate Change Conference (COP27/UNFCCC2022). The method includes three methodological steps, namely defining the topicality bifurcation points, measuring the ‘thickness’ of ‘branches’, and summarizing the meaning of individual ‘branches’, thus allowing for both topicality divergence assessment and quick enough opinion tracing. Our method integrates recursive BERTopic-based topic modeling and Pegasus-based abstractive summarization, allowing for opinions to be seen as ‘folded’, ‘unfolded’, and ‘polar’, as detected in summaries of varying length.

Keywords: cumulative deliberation, online discussions, topic modeling, abstractive summarization, BERT, Reddit, COP27
DOI: 10.55959/msu.vestnik.journ.5.2025.179208

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To cite this article: Bodrunova S. S., Blekanov I. S., Tarasov N. A. (2025) “Derevo mneniy”: metod dinamicheskogo meppinga onlayn-diskussiy na osnove neyrosetevogo tematicheskogo modelirovaniya i abstraktivnoy summarizatsii [‘Opinion tree’: a method for mapping online discussions based on neural-network topic modeling and abstractive summarization]. Vestnik Moskovskogo Universiteta. Seriya 10. Zhurnalistika 5: 179–208. DOI: 10.55959/msu.vestnik.journ.5.2025.179208