Government Health Communication During the COVID-19 Pandemic: A BERT Topic Modeling Approach
Different levels of government agencies have exerted great effort to communicate with the public during the Covid-19 pandemic on multiple social media platforms. This study uses BERT topic modeling, an artificial intelligence model, to extract topics from various public health agencies of cities, st...
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| Format: | Article |
| Language: | English |
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LibraryPress@UF
2024-05-01
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| Series: | Proceedings of the International Florida Artificial Intelligence Research Society Conference |
| Online Access: | https://journals.flvc.org/FLAIRS/article/view/135390 |
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| author | Thomas Moore-Pizon Nic DePaula Loni Hagen |
| author_facet | Thomas Moore-Pizon Nic DePaula Loni Hagen |
| author_sort | Thomas Moore-Pizon |
| collection | DOAJ |
| description | Different levels of government agencies have exerted great effort to communicate with the public during the Covid-19 pandemic on multiple social media platforms. This study uses BERT topic modeling, an artificial intelligence model, to extract topics from various public health agencies of cities, states and the federal government on Twitter and Facebook for the years 2020 and 2021. We contrast and compare major topics addressed by these agencies related to Covid-19 and the pandemic across the two major social media platforms. The findings show how we can employ BERT topic modeling to extract social media topics during a health emergency and evaluate the extent to which topics covered by these agencies address the major social and health concerns of the pandemic. |
| format | Article |
| id | doaj-art-12339a4acc76434cabcdaa7bbb484708 |
| institution | DOAJ |
| issn | 2334-0754 2334-0762 |
| language | English |
| publishDate | 2024-05-01 |
| publisher | LibraryPress@UF |
| record_format | Article |
| series | Proceedings of the International Florida Artificial Intelligence Research Society Conference |
| spelling | doaj-art-12339a4acc76434cabcdaa7bbb4847082025-08-20T03:05:39ZengLibraryPress@UFProceedings of the International Florida Artificial Intelligence Research Society Conference2334-07542334-07622024-05-013710.32473/flairs.37.1.13539071763Government Health Communication During the COVID-19 Pandemic: A BERT Topic Modeling ApproachThomas Moore-Pizon0Nic DePaula1Loni Hagen2https://orcid.org/0000-0002-6532-0852University of South FloridaSUNY Polytechnic InstituteUniversity of South FloridaDifferent levels of government agencies have exerted great effort to communicate with the public during the Covid-19 pandemic on multiple social media platforms. This study uses BERT topic modeling, an artificial intelligence model, to extract topics from various public health agencies of cities, states and the federal government on Twitter and Facebook for the years 2020 and 2021. We contrast and compare major topics addressed by these agencies related to Covid-19 and the pandemic across the two major social media platforms. The findings show how we can employ BERT topic modeling to extract social media topics during a health emergency and evaluate the extent to which topics covered by these agencies address the major social and health concerns of the pandemic.https://journals.flvc.org/FLAIRS/article/view/135390 |
| spellingShingle | Thomas Moore-Pizon Nic DePaula Loni Hagen Government Health Communication During the COVID-19 Pandemic: A BERT Topic Modeling Approach Proceedings of the International Florida Artificial Intelligence Research Society Conference |
| title | Government Health Communication During the COVID-19 Pandemic: A BERT Topic Modeling Approach |
| title_full | Government Health Communication During the COVID-19 Pandemic: A BERT Topic Modeling Approach |
| title_fullStr | Government Health Communication During the COVID-19 Pandemic: A BERT Topic Modeling Approach |
| title_full_unstemmed | Government Health Communication During the COVID-19 Pandemic: A BERT Topic Modeling Approach |
| title_short | Government Health Communication During the COVID-19 Pandemic: A BERT Topic Modeling Approach |
| title_sort | government health communication during the covid 19 pandemic a bert topic modeling approach |
| url | https://journals.flvc.org/FLAIRS/article/view/135390 |
| work_keys_str_mv | AT thomasmoorepizon governmenthealthcommunicationduringthecovid19pandemicaberttopicmodelingapproach AT nicdepaula governmenthealthcommunicationduringthecovid19pandemicaberttopicmodelingapproach AT lonihagen governmenthealthcommunicationduringthecovid19pandemicaberttopicmodelingapproach |