Medfluencer: A Network Representation of Medical Influencers' Identities and Discourse on Social Media

Autor:	Guo, Zhijin, Simpson, Edwin, Bernardi, Roberta
Rok vydání:	2024
Předmět:	Computer Science - Social and Information Networks
Druh dokumentu:	Working Paper
Popis:	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. Comment: ACM SIGKDD 2024 Workshop epiDAMIK 2024: The 7th International Workshop on Epidemiology meets Data Mining and Knowledge Discovery
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2407.05198 Zobrazit plný text záznamu View this record from Arxiv