Social media and the COVID-19: South African and Zimbabwean Netizens’ response to a pandemic
Since the end of 2019, the world faced a major health crisis in the form of the Coronavirus (COVID-19) pandemic. To mitigate the impact of the pandemic, governments across the globe instituted measures such as restricting local and international travel and in many cases, ordering citizens to stay in...
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| Language: | English |
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Indonesian Journal of Information Systems
2022
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| Online Access: | https://doi.org/10.24002/ijis.v4i1.4338 http://hdl.handle.net/11408/4837 |
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| author | Mutanga, Murimo Bethel Ureke, Oswelled Chani, Tarirai |
| author_facet | Mutanga, Murimo Bethel Ureke, Oswelled Chani, Tarirai |
| author_sort | Mutanga, Murimo Bethel |
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| description | Since the end of 2019, the world faced a major health crisis in the form of the Coronavirus (COVID-19) pandemic. To mitigate the impact of the pandemic, governments across the globe instituted measures such as restricting local and international travel and in many cases, ordering citizens to stay indoors. Considering the social and economic impact of these restrictions it becomes crucial to investigate internet citizens’ (netizens) perception about the precautionary measures adopted. The study is anchored in the digital public sphere theory, which treats social media applications as virtual platforms where netizens commune to share ideas and debate about issues that affect them. Social media platforms already have critical public views on the current pandemic. However, the majority of this data is unstructured and difficult to interpret. Natural language processing (NLP), on the other hand, makes the task of gathering and analysing vast amounts of textual data feasible. Extracting structured knowledge from natural language, however, comes with unique challenges due to diverse linguistic properties including abbreviation, spelling mistakes, punctuations, stop words and non-standard text. In this work, The Latent Dirichlet Allocation (LDA) algorithm was applied to tweeter data to extract topics discussed by netzens from Zimbabwe and South Africa. The primary focus of this paper, is to comparatively explore the variety of topics that occupied twitter communities from the two countries. We examine whether or not the national identities that define and differentiate citizens of these countries also exist on Twitter as evident in the emerging topics. Furthermore, this work investigated public opinion by analysing how citizens discuss the issues around the COVID-19 pandemic on social media |
| format | Article |
| id | ir-11408-4837 |
| institution | My University |
| language | English |
| publishDate | 2022 |
| publisher | Indonesian Journal of Information Systems |
| record_format | dspace |
| spelling | ir-11408-48372022-06-27T13:49:06Z Social media and the COVID-19: South African and Zimbabwean Netizens’ response to a pandemic Mutanga, Murimo Bethel Ureke, Oswelled Chani, Tarirai Topic extraction Natural language processing Covid19 Zimbabwe South Africa Since the end of 2019, the world faced a major health crisis in the form of the Coronavirus (COVID-19) pandemic. To mitigate the impact of the pandemic, governments across the globe instituted measures such as restricting local and international travel and in many cases, ordering citizens to stay indoors. Considering the social and economic impact of these restrictions it becomes crucial to investigate internet citizens’ (netizens) perception about the precautionary measures adopted. The study is anchored in the digital public sphere theory, which treats social media applications as virtual platforms where netizens commune to share ideas and debate about issues that affect them. Social media platforms already have critical public views on the current pandemic. However, the majority of this data is unstructured and difficult to interpret. Natural language processing (NLP), on the other hand, makes the task of gathering and analysing vast amounts of textual data feasible. Extracting structured knowledge from natural language, however, comes with unique challenges due to diverse linguistic properties including abbreviation, spelling mistakes, punctuations, stop words and non-standard text. In this work, The Latent Dirichlet Allocation (LDA) algorithm was applied to tweeter data to extract topics discussed by netzens from Zimbabwe and South Africa. The primary focus of this paper, is to comparatively explore the variety of topics that occupied twitter communities from the two countries. We examine whether or not the national identities that define and differentiate citizens of these countries also exist on Twitter as evident in the emerging topics. Furthermore, this work investigated public opinion by analysing how citizens discuss the issues around the COVID-19 pandemic on social media 2022-05-09T10:59:10Z 2022-05-09T10:59:10Z 2021 Article 2623-2308 2623-0119 https://doi.org/10.24002/ijis.v4i1.4338 http://hdl.handle.net/11408/4837 en Indonesian Journal of Information Systems;Vol. 4; No. 1: p. 1-14 open Indonesian Journal of Information Systems |
| spellingShingle | Topic extraction Natural language processing Covid19 Zimbabwe South Africa Mutanga, Murimo Bethel Ureke, Oswelled Chani, Tarirai Social media and the COVID-19: South African and Zimbabwean Netizens’ response to a pandemic |
| title | Social media and the COVID-19: South African and Zimbabwean Netizens’ response to a pandemic |
| title_full | Social media and the COVID-19: South African and Zimbabwean Netizens’ response to a pandemic |
| title_fullStr | Social media and the COVID-19: South African and Zimbabwean Netizens’ response to a pandemic |
| title_full_unstemmed | Social media and the COVID-19: South African and Zimbabwean Netizens’ response to a pandemic |
| title_short | Social media and the COVID-19: South African and Zimbabwean Netizens’ response to a pandemic |
| title_sort | social media and the covid-19: south african and zimbabwean netizens’ response to a pandemic |
| topic | Topic extraction Natural language processing Covid19 Zimbabwe South Africa |
| url | https://doi.org/10.24002/ijis.v4i1.4338 http://hdl.handle.net/11408/4837 |
| work_keys_str_mv | AT mutangamurimobethel socialmediaandthecovid19southafricanandzimbabweannetizensresponsetoapandemic AT urekeoswelled socialmediaandthecovid19southafricanandzimbabweannetizensresponsetoapandemic AT chanitarirai socialmediaandthecovid19southafricanandzimbabweannetizensresponsetoapandemic |