Cluster-Based BERTopic Modeling on Swedish COVID-19 Vaccine Posts

Summary, in English

This paper explores the prevalent themes across multiple threads on the popular Swedish discussion forum Flashback. Among its diverse array of topics, the forum actively engages users in addressing and debating questions pertaining to COVID-19 vaccines and vaccination. Through distinguishing between positive and negative perspectives within posts across 14 relevant thread discussions, we employ BERTopic, a modular topic modeling framework, which utilizes pre-trained language models and applies clustering techniques to identify prevailing topics. This enables us to conduct a nuanced exploration of overarching themes, offering valuable insights into the multifaceted nature of the discussions regarding COVID-19 vaccines and vaccination in Sweden.

• Författare: Dimitrios Kokkinakis; Mia Marie Hammarlin
• Redaktör: John Mantas; Arie Hasman; George Demiris; Kaija Saranto; Michael Marschollek; Theodoros N. Arvanitis; Ivana Ognjanovic; Arriel Benis; Parisis Gallos; Emmanouil Zoulias; Elisavet Andrikopoulou
• Avdelning/ar: Medie- och kommunikationsvetenskap; Birgit Rausing Centrum för Medicinsk Humaniora (BRCMH); Journalistik
• Publiceringsår: 2024-08
• Språk: Engelska
• Sidor: 1906-1910
• Publikation/Tidskrift/Serie: Studies in Health Technology and Informatics
• Volym: 316
• Länkar: http://dx.doi.org/10.3233/SHTI240805
• Dokumenttyp: Konferensbidrag
• Förlag: IOS Press
• Ämne: Natural Language Processing
• Nyckelord: BERTopic; Swedish dataset; topic modeling; vaccination; vaccine
• Conference name: 34th Medical Informatics Europe Conference, MIE 2024
• Conference date: 2024-08-25 - 2024-08-29
• Conference place: Athens, Greece
• Aktiv: Published
• ISBN/ISSN/Övrigt: ISSN: 1879-8365; ISSN: 0926-9630; ISBN: 9781643685335