Health protocol campaign tweet classification during the Covid-19 pandemic
Lots of information about health in social media today. Especially there is relatively a lot of negative information regarding the coverage of the current Covid-19 pandemic. Negative information has exacerbated the problem of the Covid-19 pandemic to become more complicated and has made the public p...
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| creator | Frieyadie Amsury, Fachri Saputra, Irwansyah Suryani, Ita Sagiyanto, Asriyani Liliyana Elyana, Instianti |
| description | Lots of information about health in social media today. Especially there is relatively a lot of negative information regarding the coverage of the current Covid-19 pandemic. Negative information has exacerbated the problem of the Covid-19 pandemic to become more complicated and has made the public panic and anxious. Especially the economic problems among the community regarding the limitation of the distance to interact. This study takes people’s comments from Twitter regarding public sentiment regarding the Covid-19 pandemic, seeing how much support the Indonesian people have in campaigning for health protocols on Twitter social media using the Support Vector Machine (SVM) and Naïve Bayes methods. Based on the dataset taken from Twitter in the range of December 2020 to February 2021, the average results of Indonesian people’s tweets on Twitter are mostly included in the not-campaign class, meaning that there is still a lack of awareness of the Indonesian people to help campaign health protocols during the Covid-19 pandemic on the media social Twitter. |
| doi_str_mv | 10.1063/5.0128481 |
| format | Conference Proceeding |
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Especially there is relatively a lot of negative information regarding the coverage of the current Covid-19 pandemic. Negative information has exacerbated the problem of the Covid-19 pandemic to become more complicated and has made the public panic and anxious. Especially the economic problems among the community regarding the limitation of the distance to interact. This study takes people’s comments from Twitter regarding public sentiment regarding the Covid-19 pandemic, seeing how much support the Indonesian people have in campaigning for health protocols on Twitter social media using the Support Vector Machine (SVM) and Naïve Bayes methods. Based on the dataset taken from Twitter in the range of December 2020 to February 2021, the average results of Indonesian people’s tweets on Twitter are mostly included in the not-campaign class, meaning that there is still a lack of awareness of the Indonesian people to help campaign health protocols during the Covid-19 pandemic on the media social Twitter.</description><identifier>ISSN: 0094-243X</identifier><identifier>EISSN: 1551-7616</identifier><identifier>DOI: 10.1063/5.0128481</identifier><identifier>CODEN: APCPCS</identifier><language>eng</language><publisher>Melville: American Institute of Physics</publisher><subject>Coronaviruses ; COVID-19 ; Digital media ; Pandemics ; Social networks ; Support vector machines</subject><ispartof>AIP conference proceedings, 2023, Vol.2714 (1)</ispartof><rights>Author(s)</rights><rights>2023 Author(s). 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Based on the dataset taken from Twitter in the range of December 2020 to February 2021, the average results of Indonesian people’s tweets on Twitter are mostly included in the not-campaign class, meaning that there is still a lack of awareness of the Indonesian people to help campaign health protocols during the Covid-19 pandemic on the media social Twitter.</abstract><cop>Melville</cop><pub>American Institute of Physics</pub><doi>10.1063/5.0128481</doi><tpages>8</tpages></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0094-243X |
| ispartof | AIP conference proceedings, 2023, Vol.2714 (1) |
| issn | 0094-243X 1551-7616 |
| language | eng |
| recordid | cdi_scitation_primary_10_1063_5_0128481 |
| source | AIP Journals Complete |
| subjects | Coronaviruses COVID-19 Digital media Pandemics Social networks Support vector machines |
| title | Health protocol campaign tweet classification during the Covid-19 pandemic |
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