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

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.

The Prevalence of mRNA Related Discussions During the Post-COVID-19 Era.

Vaccinations are one of the most significant interventions to public health, but vaccine hesitancy and skepticism are raising serious concerns for a portion of the population in many countries, including Sweden. In this study, we use Swedish social media data and structural topic modeling to automatically identify mRNA-vaccine related discussion themes and gain deeper insights into how people's refusal or acceptance of the mRNA technology affects vaccine uptake. Our point of departure is a scientific study published in February 2022, which seems to once again sparked further suspicion and concern and highlight the necessity to focus on issues about the nature and trustworthiness in vaccine safety.

Using a Discourse Task to Explore Semantic Ability in Persons With Cognitive Impairment.

This paper uses a discourse task to explore aspects of semantic production in persons with various degree of cognitive impairment and healthy controls. The purpose of the study was to test if an in-depth semantic analysis of a cognitive-linguistic challenging discourse task could differentiate persons with a cognitive decline from those with a stable cognitive impairment. Both quantitative measures of semantic ability, using tests of oral lexical retrieval, and qualitative analysis of a narrative were used to detect semantic difficulties.