Medfluencer: A Network Representation of Medical Influencers' Identities and Discourse on Social Media
<|reference_start|>Medfluencer: A Network Representation of Medical Influencers' Identities and Discourse on Social Media: In our study, we first constructed a dataset from the tweets of the top 100 medical influencers with the highest Influencer Score 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 actors' 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.<|reference_end|>
@article{guo2024medfluencer:,
    title={Medfluencer: A Network Representation of Medical Influencers' Identities
  and Discourse on Social Media},
    author={Zhijin Guo, Edwin Simpson, Roberta Bernardi},
    journal={arXiv preprint arXiv:2407.05198},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.05198},
    primaryClass={cs.SI}
}