Cluster-Based BERTopic Modeling on Swedish COVID-19 Vaccine Posts
Kokkinakis, Dimitrios and Hammarlin, Mia Marie LU
(2024)
34th Medical Informatics Europe Conference, MIE 2024
In Studies in Health Technology and Informatics
316.
p.1906-1910
- Abstract
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... (More)
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.
(Less)
- author
- Kokkinakis, Dimitrios
and Hammarlin, Mia Marie
LU
- organization
- publishing date
- 2024-08
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- BERTopic, Swedish dataset, topic modeling, vaccination, vaccine
- host publication
- Digital Health and Informatics Innovations for Sustainable Health Care Systems - Proceedings of MIE 2024
- series title
- Studies in Health Technology and Informatics
- editor
- Mantas, John ; Hasman, Arie ; Demiris, George ; Saranto, Kaija ; Marschollek, Michael ; Arvanitis, Theodoros N. ; Ognjanovic, Ivana ; Benis, Arriel ; Gallos, Parisis ; Zoulias, Emmanouil and Andrikopoulou, Elisavet
- volume
- 316
- pages
- 5 pages
- publisher
- IOS Press
- conference name
- 34th Medical Informatics Europe Conference, MIE 2024
- conference location
- Athens, Greece
- conference dates
- 2024-08-25 - 2024-08-29
- external identifiers
-
- scopus:85202007064
- pmid:39176864
- ISSN
- 1879-8365
- 0926-9630
- ISBN
- 9781643685335
- DOI
- 10.3233/SHTI240805
- language
- English
- LU publication?
- yes
- id
- 4ac1cb7c-b815-4d42-82b5-e4502fe6530c
- date added to LUP
- 2024-10-30 13:59:34
- date last changed
- 2025-07-10 12:30:59
@inproceedings{4ac1cb7c-b815-4d42-82b5-e4502fe6530c,
abstract = {{<p>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.</p>}},
author = {{Kokkinakis, Dimitrios and Hammarlin, Mia Marie}},
booktitle = {{Digital Health and Informatics Innovations for Sustainable Health Care Systems - Proceedings of MIE 2024}},
editor = {{Mantas, John and Hasman, Arie and Demiris, George and Saranto, Kaija and Marschollek, Michael and Arvanitis, Theodoros N. and Ognjanovic, Ivana and Benis, Arriel and Gallos, Parisis and Zoulias, Emmanouil and Andrikopoulou, Elisavet}},
isbn = {{9781643685335}},
issn = {{1879-8365}},
keywords = {{BERTopic; Swedish dataset; topic modeling; vaccination; vaccine}},
language = {{eng}},
pages = {{1906--1910}},
publisher = {{IOS Press}},
series = {{Studies in Health Technology and Informatics}},
title = {{Cluster-Based BERTopic Modeling on Swedish COVID-19 Vaccine Posts}},
url = {{http://dx.doi.org/10.3233/SHTI240805}},
doi = {{10.3233/SHTI240805}},
volume = {{316}},
year = {{2024}},
}