Algorithmic curation is a growing influence on our information flows as it complements and sometimes supplants traditional mass media and personal information sharing. One of the primary agents of this rise in algorithmically-curated information flows is the Facebook News Feed, a onetime source of primarily entertainment that has, as of late, taken large strides towards the news business. It is fair to say that Facebook has a huge influence on our information, one that will likely expand in the future; even if not Facebook, similar systems will rule our information. Yet, we know next to nothing about how they work, as the algorithms that power them are sealed inside a black box. This thesis approaches the Facebook News Feed through a mix of qualitative and quantitative methods in a process dubbed “Negative Reverse Engineering” in an attempt to gain access to the contents of the black box not through traditional technical means, but through an analysis of Facebook’s values structure and needs. Components include an extensive, cross-disciplinary review of the literature, an experiment based around the generation of filter bubbles through the application of negative pressure, a grounded content analysis of Facebook’s statements and documents, an autoethnography of Facebook use, and a regression analysis of Facebook under duress. From this data, a Hierarchy of Needs for the News Feed is created, rejecting the model of News Feed filtering as an equation in favor of a holistic, values-based model.
|Commitee:||Karpf, David A., Thorson, Emily A.|
|School:||The George Washington University|
|Department:||Media and Public Affairs|
|School Location:||United States -- District of Columbia|
|Source:||MAI 54/05M(E), Masters Abstracts International|
|Subjects:||Journalism, Communication, Information science|
|Keywords:||Algorithmic curation, Algorithms, Facebook, Filter bubbles, Reverse engineering, Social media|
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