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Title:
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FRAMING MEDIA COVERAGE OF EBOLA 2014 OUTBREAK AND ITS ECONOMIC IMPACT: A LATENT DIRICHLET ALLOCATION TOPIC MODELLING APPROACH |
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Author(s):
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Stephen Nabareseh, Eric Afful-Dadzie, Petr Klimek, Lucia Hasa |
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ISBN:
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978-989-8533-60-9 |
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Editors:
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Piet Kommers and Pedro Isaías |
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Year:
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2017 |
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Edition:
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Single |
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Keywords:
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Ebola Virus Disease (EVD), Topic Modelling, News Framing, Latent Dirichlet Allocation (LDA), Text Mining; Media |
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Type:
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Full Paper |
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First Page:
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73 |
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Last Page:
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80 |
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Language:
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English |
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Cover:
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Full Contents:
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click to dowload 
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Paper Abstract:
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The 2014 outbreak of the Ebola Virus Disease (EVD) mainly in parts of West Africa has been one of the most widely covered epidemics in history by the media. As the disease continues to receive traction in major news outlets around the world, the objective of this paper was to create on a monthly basis, a timeline story of events capturing the central themes as far as the outbreak and its response in local and international media was concerned. The paper used the Latent Dirichlet Allocation (LDA) algorithm of automated topic modelling and Text Mining techniques to explore the essential themes in news coverage as reported in international and local media of two of the most affected countries; Liberia and Sierra Leone. The topics were compared for similarities, disparities, inconsistencies and the general emotions behind the topics. The paper found that international media unlike their local counterparts reported more about the history, origin, causes and symptoms of the disease. |
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