Medfluencer: A Network Representation of Medical Influencers' Identities and Discourse on Social Media
Fuente:
arXiv
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866917737925705728 |
|---|---|
| author | Guo, Zhijin Simpson, Edwin Bernardi, Roberta |
| author_facet | Guo, Zhijin Simpson, Edwin Bernardi, Roberta |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_05198 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Medfluencer: A Network Representation of Medical Influencers' Identities and Discourse on Social Media Guo, Zhijin Simpson, Edwin Bernardi, Roberta Social and Information Networks 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. |
| title | Medfluencer: A Network Representation of Medical Influencers' Identities and Discourse on Social Media |
| topic | Social and Information Networks |
| url | https://arxiv.org/abs/2407.05198 |