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    Bursting your (filter) bubble how personalization is changing the way you perceive reality from the information you consume on social media

    Bishop, Alexandra Celia
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    [PDF] BishopAlexandraResearch.pdf (435.5Kb)
    Date
    2019
    Format
    Thesis
    Metadata
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    Abstract
    Personalization filters work behind-the-scenes to curate the information users encounter online. This has influenced users' online information diets by uprooting traditional gatekeeping practices and socialization processes. Through the theoretical lenses of network gatekeeping identification and social construction, this study employs qualitative methodology, more specifically interviews, to answer how Facebook news consumers make meaning of the reality they receive through personalization filters on Facebook, to further understand how and why participants perform network gatekeeping at an individual level, how news content shapes participants' likes and beliefs, and how participants negotiate, understand, and potentially consume contradictory messages presented to them through Facebook. The findings indicate that online news consumers on Facebook prefer to remain within the confines of their individual "filter bubbles", through the constant consumption of information, that is tailored to pre-existing beliefs. The over-exposure to one-sided information further indoctrinates participants and limits their exposure to new ideas by prohibiting contradictory information from entering the gate. The effect personalization filters will have on the future of gatekeeping and socialization is unknown, but the findings suggest that changes should be made to prevent the rise of the already growing ideological divide in the United States. The 2016 Presidential Election in the United States is a prime example of the impending threat personalization filters have to the democratic process.
    URI
    https://hdl.handle.net/10355/70128
    Degree
    M.A.
    Thesis Department
    Journalism (MU)
    Rights
    OpenAccess.
    This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 License.
    Collections
    • Journalism electronic theses and dissertations (MU)
    • 2019 MU theses - Freely available online

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