1-D TV: A Computational Investigation into the Lexical Representation of Black Womanhood In Reality Television News
Tri-College (Bryn Mawr, Haverford, and Swarthmore Colleges). Department of Linguistics
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It is well-established that when categorizing lexical associations of words in news corpora, women and minorities tend to be associated with negative terms (Manziniet al. 2019). This harm is also carried through other forms of media. For instance, Black women on television have been historically depicted as one dimensional characters, often forced into categories of strict binaries. Commonly, they are either extremely educated or they dropped out of school, either they are ambitious or they have lost all enthusiasm, either they are completely desexualized or they are hypersexualized, either they are always compliant or they are always aggressive (Boylorn 2008). While these depictions are known to cause harm, racism and sexism are not necessarily so overt, and more work is needed to quantify the effects and spread of stereotypes relating to intersections of identities. Through this thesis, I use the context of reality television to examine how racial representations in media can influence people’s perceptions of Black womanhood. I begin with background information on some of the effects of media consumption and with a high-level computational overview of how words can be represented as vectors to quantify prejudicial bias in text representations. Afterwards, I conduct a literature review exploring some of the ways previous researchers (Parthasarthi et al. 2019; Garget al. 2018) have measured bias in digital media both through text and over time. Then in order to understand more about the complexities of this task, I explore away in which word embeddings can be generated by using the Word2vec algorithm (Mikolov et al. 2013a) and visualized through vector representation tools. I conclude by addressing the challenges of my experiment and suggesting future improvements to this project.