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

Zhijin Guo; Edwin D. Simpson; Roberta Bernardi

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

Zhijin Guo, Edwin D. Simpson, Roberta Bernardi

Research output: Contribution to conference › Conference Paper › peer-review

35 Downloads (Pure)

Abstract

In our study, we first constructed a dataset from the tweets ofthe top 100 medical influencers with the highest Influencer Score[14] during the COVID-19 pandemic. This dataset was then usedto construct a socio-semantic network, mapping both their identities and key topics, which are crucial for understanding theirimpact on public health discourse. To achieve this, we developeda few-shot multi-label classifier to identify influencers and theirnetwork actors’ identities, employed BERTopic for extracting thematic content, and integrated these components into a networkmodel to analyze their impact on health discourse. To ensure thereproducibility of our results, we have made the code available athttps://github.com/ZhijinGuo/Medinfluencer.

Original language	English
Number of pages	8
Publication status	Published - 4 Jul 2024
Event	epiDAMIK 2024: : The 7th International workshop on Epidemiology meets data Miningand Knowledge discovery - https://epidamik.github.io/, Barcelona, Spain Duration: 26 Aug 2024 → 26 Aug 2024

Workshop

Workshop	epiDAMIK 2024:
Abbreviated title	epiDAMIK
Country/Territory	Spain
City	Barcelona
Period	26/08/24 → 26/08/24

Research Groups and Themes

Jean Golding
social network
Covid-19
large language models
social media
MGMT Operations and Management Science
social network
Covid-19
large language models
social media
Elizabeth Blackwell Institute
social network
Covid-19
large language models
social media
Digital Societies
social network
Covid-19
large language models
social media
MGMT theme Innovation and Digitalisation
social network
Covid-19
large language models
social media
MGMT theme Public Services Governance and Management
social network
Covid-19
large language models
social media
Health and Wellbeing
social network
Covid-19`
large language models
social media

Keywords

social network
Covid-19
large language models (LLMs)
social media

Access to Document

Full-text PDF (accepted manuscript) Accepted author manuscript, 1.29 MBLicence: CC BY

https://openreview.net/pdf?id=StgezOmzHdLicence: CC BY

Handle.net

Persistent link

“Medfluencer: a socio-semantic network analysis method to investigate how medical influencers affect health discourse on social media
Bernardi, R. (Principal Investigator), Simpson, E. D. (Co-Principal Investigator) & Guo, Z. (Researcher)
20/11/23 → 31/07/24
Project: Research

Cite this

@conference{8461593e11574bc080c9c462b60f4884,

title = "Medfluencer: A Network Representation of Medical Influencers{\textquoteright} Identities and Discourse on Social Media",

abstract = "In our study, we first constructed a dataset from the tweets ofthe top 100 medical influencers with the highest Influencer Score[14] during the COVID-19 pandemic. This dataset was then usedto construct a socio-semantic network, mapping both their identities and key topics, which are crucial for understanding theirimpact on public health discourse. To achieve this, we developeda few-shot multi-label classifier to identify influencers and theirnetwork actors{\textquoteright} identities, employed BERTopic for extracting thematic content, and integrated these components into a networkmodel to analyze their impact on health discourse. To ensure thereproducibility of our results, we have made the code available athttps://github.com/ZhijinGuo/Medinfluencer.",

keywords = "social network, Covid-19, large language models (LLMs), social media",

author = "Zhijin Guo and Simpson, {Edwin D.} and Roberta Bernardi",

year = "2024",

month = jul,

day = "4",

language = "English",

note = "epiDAMIK 2024: : The 7th International workshop on Epidemiology meets data Miningand Knowledge discovery, epiDAMIK ; Conference date: 26-08-2024 Through 26-08-2024",

}

TY - CONF

T1 - Medfluencer

T2 - epiDAMIK 2024:

AU - Guo, Zhijin

AU - Simpson, Edwin D.

AU - Bernardi, Roberta

PY - 2024/7/4

Y1 - 2024/7/4

N2 - In our study, we first constructed a dataset from the tweets ofthe top 100 medical influencers with the highest Influencer Score[14] during the COVID-19 pandemic. This dataset was then usedto construct a socio-semantic network, mapping both their identities and key topics, which are crucial for understanding theirimpact on public health discourse. To achieve this, we developeda few-shot multi-label classifier to identify influencers and theirnetwork actors’ identities, employed BERTopic for extracting thematic content, and integrated these components into a networkmodel to analyze their impact on health discourse. To ensure thereproducibility of our results, we have made the code available athttps://github.com/ZhijinGuo/Medinfluencer.

AB - In our study, we first constructed a dataset from the tweets ofthe top 100 medical influencers with the highest Influencer Score[14] during the COVID-19 pandemic. This dataset was then usedto construct a socio-semantic network, mapping both their identities and key topics, which are crucial for understanding theirimpact on public health discourse. To achieve this, we developeda few-shot multi-label classifier to identify influencers and theirnetwork actors’ identities, employed BERTopic for extracting thematic content, and integrated these components into a networkmodel to analyze their impact on health discourse. To ensure thereproducibility of our results, we have made the code available athttps://github.com/ZhijinGuo/Medinfluencer.

KW - social network

KW - Covid-19

KW - large language models (LLMs)

KW - social media

M3 - Conference Paper

Y2 - 26 August 2024 through 26 August 2024

ER -

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

Abstract

Workshop

Research Groups and Themes

Keywords

Access to Document

Handle.net

Fingerprint

Projects

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

Cite this