TY - GEN

T1 - Mapping Opinion Cumulation: Topic Modeling-Based Dynamic Summarization of User Discussions on Social Networks

AU - Блеканов, Иван Станиславович

AU - Тарасов, Никита Андреевич

AU - Бодрунова, Светлана Сергеевна

AU - Сергеев, Сергей Львович

N1 - Conference code: 25

PY - 2023

Y1 - 2023

N2 - 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.

AB - 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.

KW - Abstractive summarization

KW - Cumulative deliberation

KW - Dynamic summarization

KW - Mapping discussions

KW - Opinion cumulation

KW - Topic modeling

UR - https://link.springer.com/chapter/10.1007/978-3-031-35915-6_3

UR - https://www.mendeley.com/catalogue/25e9215a-a7e8-3e3c-abb0-3308d6ffea17/

U2 - 10.1007/978-3-031-35915-6_3

DO - 10.1007/978-3-031-35915-6_3

M3 - Conference contribution

SN - 978-3-031-35914-9

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 25

EP - 40

BT - Social Computing and Social Media. HCII 2023

PB - Springer Nature

T2 - 25TH INTERNATIONAL CONFERENCE ON HUMAN-COMPUTER INTERACTION

Y2 - 23 July 2023 through 28 July 2023

ER -