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 unders...
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| creator | Guo, Zhijin Simpson, Edwin Bernardi, Roberta |
| description | 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. |
| doi_str_mv | 10.48550/arxiv.2407.05198 |
| format | Article |
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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.</description><identifier>DOI: 10.48550/arxiv.2407.05198</identifier><language>eng</language><subject>Computer Science - Social and Information Networks</subject><creationdate>2024-07</creationdate><rights>http://arxiv.org/licenses/nonexclusive-distrib/1.0</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,776,881</link.rule.ids><linktorsrc>$$Uhttps://arxiv.org/abs/2407.05198$$EView_record_in_Cornell_University$$FView_record_in_$$GCornell_University$$Hfree_for_read</linktorsrc><backlink>$$Uhttps://doi.org/10.48550/arXiv.2407.05198$$DView paper in arXiv$$Hfree_for_read</backlink></links><search><creatorcontrib>Guo, Zhijin</creatorcontrib><creatorcontrib>Simpson, Edwin</creatorcontrib><creatorcontrib>Bernardi, Roberta</creatorcontrib><title>Medfluencer: A Network Representation of Medical Influencers' Identities and Discourse on Social Media</title><description>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.</description><subject>Computer Science - Social and Information Networks</subject><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>GOX</sourceid><recordid>eNqFzjsPgjAUhuEuDkb9AU6ezUksChHdjJfIoIO6k6acJidiS1rw8u8tRGenb3m-5GVsGPIgSuKYT4V90SOYRXwR8DhcJl2mjpirokYt0a5gDSesnsbe4IylRYe6EhUZDUaBhyRFAan-eTeGNPeEKkIHQuewJSdNbR2C_1yMJO-bn-izjhKFw8F3e2y03103h0lblJWW7sK-s6Ysa8vm_8UH-0JEXw</recordid><startdate>20240706</startdate><enddate>20240706</enddate><creator>Guo, Zhijin</creator><creator>Simpson, Edwin</creator><creator>Bernardi, Roberta</creator><scope>AKY</scope><scope>GOX</scope></search><sort><creationdate>20240706</creationdate><title>Medfluencer: A Network Representation of Medical Influencers' Identities and Discourse on Social Media</title><author>Guo, Zhijin ; Simpson, Edwin ; Bernardi, Roberta</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-arxiv_primary_2407_051983</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Computer Science - Social and Information Networks</topic><toplevel>online_resources</toplevel><creatorcontrib>Guo, Zhijin</creatorcontrib><creatorcontrib>Simpson, Edwin</creatorcontrib><creatorcontrib>Bernardi, Roberta</creatorcontrib><collection>arXiv Computer Science</collection><collection>arXiv.org</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Guo, Zhijin</au><au>Simpson, Edwin</au><au>Bernardi, Roberta</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Medfluencer: A Network Representation of Medical Influencers' Identities and Discourse on Social Media</atitle><date>2024-07-06</date><risdate>2024</risdate><abstract>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.</abstract><doi>10.48550/arxiv.2407.05198</doi><oa>free_for_read</oa></addata></record> |
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| title | Medfluencer: A Network Representation of Medical Influencers' Identities and Discourse on Social Media |
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