Medfluencer: A Network Representation of Medical Influencers’ Identities and Discourse on Social Media
Keywords: social network, covid-19, social media, large language models
Abstract: 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 authori- ties. In our study, we first constructed a dataset from the tweets of the top 100 medical influencers with the highest Influencer Score [14] during the COVID-19 pandemic. This dataset was then used to construct a socio-semantic network, mapping both their identities and key topics, which are crucial for understanding their impact on public health discourse. To achieve this, we developed a few-shot multi-label classifier to identify influencers and their network ac- tors’ identities, employed BERTopic for extracting thematic content, and integrated these components into a network model to analyze their impact on health discourse. To ensure the reproducibility of our results, we have made the code available at https://github.com/ZhijinGuo/Medinfluencer.
Submission Number: 3
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