This dissertation used queer rhetoric as a lens for studying queering gender norms on Instagram by using Lady Gaga's, Nicki Minaj's, and fan posts as case studies. The research considers how celebrities may use social media, like Instagram, for queering gender norms, and what this might look like. This research also aimed to better understand if and how fans may take up celebrities' efforts at queering gender norms and, in turn, queer gender norms in their own Instagram posts where they tag Gaga or Minaj. To conduct this research, I took a multimodal methodological approach and collected and coded 1,000 posts from Gaga and Minaj, respectively, and 1,000 posts that used the hashtag Gaga and another 1,000 posts that used the hashtag Minaj. My findings suggested that Gaga and Minaj do not engage in the queering of gender norms as frequently as anticipated, and when they do it is often in relation to their public, staged performances as musicians. Furthermore, Gaga also spoke on issues relating to gender and marriage equality whereas Minaj also spoke on issues relating to racial equality. The data collected on fans was inconclusive in part because of the large number of spam posts and also because, without interviewing fans, it was difficult to discern whether they were taking up celebrity messages in their posts given information shared in the photo and in the caption. However, I was able to note that, most often, fans were engaging with celebrities by expressing admiration. This research is useful for considering how gender performance manifests on Instagram, and possible ways celebrities can utilize Instagram to queer gender norms as well as promote other messages. With regard to fan posts, I argue for continued research in ways to support fans becoming critical rather than passive consumers of celebrity culture.
| Identifer | oai:union.ndltd.org:ucf.edu/oai:stars.library.ucf.edu:etd-6797 |
| Date | 01 January 2018 |
| Creators | Dieterle, Brandy |
| Publisher | STARS |
| Source Sets | University of Central Florida |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Electronic Theses and Dissertations |
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