Systematic Delineation of Media Polarity on COVID-19 Vaccines in Africa: Computational Linguistic Modeling Study

Gbashi, Sefater; Adebo, Oluwafemi Ayodeji; Doorsamy, Wesley; Njobeh, Patrick Berka

doi:10.2196/22916

Cited by 26 publications

(24 citation statements)

References 67 publications

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“…Gbashi et al found that most Google news (569) and Twitter posts (637) between February and May 2020 were generally passive to positive towards COVID-19 vaccines in Africa. 50 In contrast, in November and December, Talkwalker media monitoring sentiment analysis from UNICEF found that less than 10% of online posts displayed positive sentiment in 2020. 27,31 Dereje et al in Ethiopia (n= 422) found higher vaccine refusal among participants that primarily receive information from social media (internet) as compared to TV/radio.…”

Section: The Degree Of Community Involvement and Social Norms Influenced Vaccine Acceptancementioning

confidence: 99%

Data Synthesis: COVID-19 Vaccine Perceptions in Africa: Social and Behavioural Science Data, March 2020-March 2021

Tulloch¹,

Jong²,

Bardosh³

2021

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Safe and effective vaccines against COVID-19 are seen as a critical path to ending the pandemic. This synthesis brings together data related to public perceptions about COVID-19 vaccines collected between March 2020 and March 2021 in 22 countries in Africa. It provides an overview of the data (primarily from cross-sectional perception surveys), identifies knowledge and research gaps and presents some limitations of translating the available evidence to inform local operational decisions. The synthesis is intended for those designing and delivering vaccination programmes and COVID-19 risk communication and community engagement (RCCE). 5 large-scale surveys are included with over 12 million respondents in 22 central, eastern, western and southern African countries (note: one major study accounts for more than 10 million participants); data from 14 peer-reviewed questionnaire surveys in 8 countries with n=9,600 participants and 15 social media monitoring, qualitative and community feedback studies. Sample sizes are provided in the first reference for each study and in Table 13 at the end of this document. The data largely predates vaccination campaigns that generally started in the first quarter of 2021. Perceptions will change and further syntheses, that represent the whole continent including North Africa, are planned. This review is part of the Social Science in Humanitarian Action Platform (SSHAP) series on COVID-19 vaccines. It was developed for SSHAP by Anthrologica. It was written by Kevin Bardosh (University of Washington), Tamara Roldan de Jong and Olivia Tulloch (Anthrologica), it was reviewed by colleagues from PERC, LSHTM, IRD, and UNICEF (see acknowledgments) and received coordination support from the RCCE Collective Service. It is the responsibility of SSHAP.

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Section: The Degree Of Community Involvement and Social Norms Influenced Vaccine Acceptancementioning

confidence: 99%

Data Synthesis: COVID-19 Vaccine Perceptions in Africa: Social and Behavioural Science Data, March 2020-March 2021

Tulloch¹,

Jong²,

Bardosh³

2021

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“…Also, several sentiment analysis studies regarding the COVID-19 vaccine have been conducted as of early 2021. A study by Gbashi et al (2021) focused on detecting the opinion of media polarity on COVID-19 vaccine in Africa with Twitter and Google News articles [ 20 ]. Furthermore, additional research investigated public sentiment in India [ 21 , 22 ], Indonesia [ 23 ], and China [ 24 ].…”

Section: Introductionmentioning

confidence: 99%

Public sentiment analysis and topic modeling regarding COVID-19 vaccines on the Reddit social media platform: A call to action for strengthening vaccine confidence

Melton

Olusanya

Ammar

et al. 2021

Journal of Infection and Public Health

151

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Background The COVID-19 outbreak fueled one of the most rapid vaccine developments in history. However, misinformation spread through online social media often leads to negative vaccine sentiment and hesitancy. Methods To investigate COVID-19 vaccine-related discussion in social media, we conducted a sentiment analysis and Latent Dirichlet Allocation topic modeling on textual data collected from 13 Reddit communities focusing on the COVID-19 vaccine from Dec 1, 2020, to May 15, 2021. Data were aggregated and analyzed by month to detect changes in any sentiment and latent topics. Results Polarity analysis suggested these communities expressed more positive sentiment than negative regarding the vaccine-related discussions and has remained static over time. Topic modeling revealed community members mainly focused on side effects rather than outlandish conspiracy theories. Conclusion Covid-19 vaccine-related content from 13 subreddits show that the sentiments expressed in these communities are overall more positive than negative and have not meaningfully changed since December 2020. Keywords indicating vaccine hesitancy were detected throughout the LDA topic modeling. Public sentiment and topic modeling analysis regarding vaccines could facilitate the implementation of appropriate messaging, digital interventions, and new policies to promote vaccine confidence.

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“…In general, existing studies have investigated people’s reactions towards COVID-19 vaccines, with a geographical emphasis on the United States [ 14 - 19 ]. Some papers have also studied other countries in the world, including China [ 20 ], South Africa [ 21 ], Australia [ 22 ], the United Kingdom [ 14 , 23 ], Canada [ 24 ], and Africa [ 25 ], and to a global scale [ 26 ]. However, the study period of these works is relatively limited to or predominantly focused on the early stage of the pandemic or up to the end of 2020.…”

Section: Introductionmentioning

confidence: 99%

Revealing Public Opinion Towards COVID-19 Vaccines With Twitter Data in the United States: Spatiotemporal Perspective

Hu¹,

Wang²,

Luo³

et al. 2021

J Med Internet Res

122

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Background The COVID-19 pandemic has imposed a large, initially uncontrollable, public health crisis both in the United States and across the world, with experts looking to vaccines as the ultimate mechanism of defense. The development and deployment of COVID-19 vaccines have been rapidly advancing via global efforts. Hence, it is crucial for governments, public health officials, and policy makers to understand public attitudes and opinions towards vaccines, such that effective interventions and educational campaigns can be designed to promote vaccine acceptance. Objective The aim of this study was to investigate public opinion and perception on COVID-19 vaccines in the United States. We investigated the spatiotemporal trends of public sentiment and emotion towards COVID-19 vaccines and analyzed how such trends relate to popular topics found on Twitter. Methods We collected over 300,000 geotagged tweets in the United States from March 1, 2020 to February 28, 2021. We examined the spatiotemporal patterns of public sentiment and emotion over time at both national and state scales and identified 3 phases along the pandemic timeline with sharp changes in public sentiment and emotion. Using sentiment analysis, emotion analysis (with cloud mapping of keywords), and topic modeling, we further identified 11 key events and major topics as the potential drivers to such changes. Results An increasing trend in positive sentiment in conjunction with a decrease in negative sentiment were generally observed in most states, reflecting the rising confidence and anticipation of the public towards vaccines. The overall tendency of the 8 types of emotion implies that the public trusts and anticipates the vaccine. This is accompanied by a mixture of fear, sadness, and anger. Critical social or international events or announcements by political leaders and authorities may have potential impacts on public opinion towards vaccines. These factors help identify underlying themes and validate insights from the analysis. Conclusions The analyses of near real-time social media big data benefit public health authorities by enabling them to monitor public attitudes and opinions towards vaccine-related information in a geo-aware manner, address the concerns of vaccine skeptics, and promote the confidence that individuals within a certain region or community have towards vaccines.

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Systematic Delineation of Media Polarity on COVID-19 Vaccines in Africa: Computational Linguistic Modeling Study

Cited by 26 publications

References 67 publications

Data Synthesis: COVID-19 Vaccine Perceptions in Africa: Social and Behavioural Science Data, March 2020-March 2021

Data Synthesis: COVID-19 Vaccine Perceptions in Africa: Social and Behavioural Science Data, March 2020-March 2021

Public sentiment analysis and topic modeling regarding COVID-19 vaccines on the Reddit social media platform: A call to action for strengthening vaccine confidence

Revealing Public Opinion Towards COVID-19 Vaccines With Twitter Data in the United States: Spatiotemporal Perspective

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