Abstract
In the recent years, a lot of methods have been proposed for detection of topicality of user discussions. Recently, the scholars have suggested approaches to tracing topicality evolution, including dynamic topic modeling. However, these approaches are overwhelmingly limited by representation of topics via lists of top words, which only hint to possible contents of topics and does not allow for real mapping of opinion cumulation [1]. We suggest a methodology for discussion mapping that combines neural-network-based encoding of user posts, HDBSCAN-based topic modeling, and abstractive summarization to map large-scale online discussions and trace bifurcation points in opinion cumulation. We test the proposed method on a mid-range dataset on climate change from Reddit and show how discussions may be summarized in a feasible and easily accessible way. Among the rest, we show that the bifurcation points in topicality are often followed by growth of a given topic, which may in future allow for predicting discussion outbursts.
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Acknowledgements
This research has been supported in full by Russian Science Foundation, grant 21-18-00454 (2021–2023).
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Blekanov, I.S., Tarasov, N., Bodrunova, S.S., Sergeev, S.L. (2023). Mapping Opinion Cumulation: Topic Modeling-Based Dynamic Summarization of User Discussions on Social Networks. In: Coman, A., Vasilache, S. (eds) Social Computing and Social Media. HCII 2023. Lecture Notes in Computer Science, vol 14025. Springer, Cham. https://doi.org/10.1007/978-3-031-35915-6_3
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