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Global Public Health
An International Journal for Research, Policy and Practice
Volume 16, 2021 - Issue 8-9: Politics and Pandemics
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Articles

WHO, COVID-19, and Taiwan as the Ghost Island

Pages 1267-1282 | Received 14 Oct 2020, Accepted 07 Feb 2021, Published online: 26 Feb 2021

Figures & data

Figure 1. (a) Word cloud of most commonly used words for Dr Shih-Chung Chen, Minister of Health and Welfare, and MOFA's speeches regarding Taiwan's participation in the World Health Organisation. (b) Bar charts of top-15 most commonly used words for Dr Shih-Chung Chen, Minister of Health and Welfare, and MOFA's speeches regarding Taiwan's participation in the World Health Organisation.

Figure 1. (a) Word cloud of most commonly used words for Dr Shih-Chung Chen, Minister of Health and Welfare, and MOFA's speeches regarding Taiwan's participation in the World Health Organisation. (b) Bar charts of top-15 most commonly used words for Dr Shih-Chung Chen, Minister of Health and Welfare, and MOFA's speeches regarding Taiwan's participation in the World Health Organisation.

Figure 2. Number of tweets per week from President Tsai Ing-Wen, Ministry of Foreign Affairs, and Ministry of Health and Welfare, Taiwan.

Figure 2. Number of tweets per week from President Tsai Ing-Wen, Ministry of Foreign Affairs, and Ministry of Health and Welfare, Taiwan.

Figure 3. Proportion of sentiments expressed among corpus of tweets including ‘#TaiwanCanHelp’ and ‘#TaiwanIsHelping’ from the Government and the Twittersphere.

Figure 3. Proportion of sentiments expressed among corpus of tweets including ‘#TaiwanCanHelp’ and ‘#TaiwanIsHelping’ from the Government and the Twittersphere.

Figure 4. (a) Latent Dirichlet Allocation (LDA) analysis on underlying topics arising from the tweet corpus from the Government. (b) Latent Dirichlet Allocation (LDA) analysis on underlying topics arising from the tweet corpus from the Twittersphere.

Figure 4. (a) Latent Dirichlet Allocation (LDA) analysis on underlying topics arising from the tweet corpus from the Government. (b) Latent Dirichlet Allocation (LDA) analysis on underlying topics arising from the tweet corpus from the Twittersphere.

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