“Medfluencer: a socio-semantic network analysis method to investigate how medical influencers affect health discourse on social media

Medical influencers on social media shape attitudes towards medical interventions but may also spread misinformation. Understanding their influence is crucial amidst growing mistrust in health authorities. We used a Twitter dataset of the top 100 medical influencers during Covid-19 to construct a socio-semantic network, mapping both their identities and key topics. These serve as vital indicators of their influence on public health discourse. We developed a classifier to identify influencers and their network actors, used BERTopic to identify influencers’ topics, and mapped their identities and topics into a network.

BERTopic captures deeper text meanings, essential for understanding conversation context and providing clear topics, especially in short texts like social media posts
Mapping actors' multiple identities (e.g., occupations) reveals their influence and how they adapt discourse for different audiences based on group affiliations