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    • 2017 MU theses - Freely available online
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    Framing of immigrants and refugees : a content analysis of mainstream and partisan news coverage of immigration

    Reed, Haley (Haley Patricia)
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    [PDF] public.pdf (5.959Kb)
    [PDF] research.pdf (1.134Mb)
    Date
    2017
    Format
    Thesis
    Metadata
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    Abstract
    This study examined the content that shaped people's perspective about Muslim immigration during the 2016 U.S. presidential election. A quantitative content analysis was performed to identify the primary and secondary frames in the sample of content and to identify if the members of the Islamophobia network were used as sources or mentioned in each selected story. The news articles with the highest engagement on Facebook about Muslim immigration from the first GOP debate on Aug. 5, 2015 to the inauguration of President Trump on Jan. 27, 2017 were analyzed using a content analysis tool, Buzzsumo. 50 news stories from 10 news outlets were analyzed. The news outlets consisted of mainstream, right-leaning and left-leaning partisan news outlets. Results showed that right-leaning news outlets were more likely to frame immigrants and refugees as a risk to Western society and America, while left-leaning news outlets framed immigrants and refugees in news stories regarding their human rights. The members of the Islamophobia network were not found as sources in the sample of content. Further research found the presence of the Islamophobia network in news articles that received lower Facebook engagement than articles included in this study. A call for further research between the connection of the Islamophobia network and politicians concludes this study.
    URI
    https://hdl.handle.net/10355/63552
    https://doi.org/10.32469/10355/63552
    Degree
    M.A.
    Thesis Department
    Journalism (MU)
    Rights
    OpenAccess.
    This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 License.
    Collections
    • 2017 MU theses - Freely available online
    • Journalism electronic theses and dissertations (MU)

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