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Voters' Sentiment And Propensity To Vote In The Wake Of Internal Squabbling In Election Management Bodies: The Case Of Ghana'S Electoral Commission

Authors: Kyeremeh, Henry;

Voters' Sentiment And Propensity To Vote In The Wake Of Internal Squabbling In Election Management Bodies: The Case Of Ghana'S Electoral Commission

Abstract

ABSTRACT: The paper explores the implications of the alleged administrative infractions at Ghana’s Electoral Commission. We combine qualitative and quantitative approaches to address the issue as follows. First, we employ machine learning techniques to express the sentiment of Ghanaians towards the Commission using Facebook comments from different public sources. Second, we use data from an online survey to examine the propensity of Ghanaians to vote in future elections. The sentiment analysis allowed for the comparison of favourable, unfavourable and neutral Facebook comments and showed that 40 percent of comments were unfavourable whereas the neutral and favourable accounted for 27 and 33 percent respectively. However, the quantitative analysis showed that those respondents who see the Commission as the reason for the previous peaceful elections have a significant and positive propensity to vote in 2020 elections. Similarly, those respondents who see magnanimity of the Ghanaian as the reason for the many peaceful polls are more likely to vote in 2020 elections. It is worth noting, however, that the redeeming of the image of the Commission is imperative as the impasse has dwindled public faith in the Commission significantly.

Keywords

Ghana, Electoral Commission, Sentiment Analysis, Facebook, Perception, voting

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This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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