Using data collected from the online dating site Match.com, this paper performs a content analysis examining the relationships between race, gender, and sexuality as both independent variables and as intersections on impression management strategies in online dating. Impression management strategies form a foundational core of how people interact with others in social situations. This analysis focuses on impression management strategies by examining how people advertise their body type in a public arena. Analysis also draws upon the types of bodies these people desire in an ideal date, as a second method of looking at the norms surrounding the ideal body type for a given group. Drawing upon intersectionality theories, this paper looks at potential biases in previous online dating literature towards white heterosexuals. Taking this idea into account, this analysis utilizes 892 profiles from major urban centers within the United States, approximately equal in the numbers of whites and blacks, gay/lesbians and heterosexuals, and men and women, in order to examine underrepresented populations in previous online dating literature. Findings show that body type norms based on intersectional race and gender literature appear to be more accurate predictors of proclaimed body type than only those using gender literatures. In addition, sexuality, race, and gender interactions appear to have an effect in the terminology an online dater uses in describing both themselves and the types of bodies desired in ideal dates. Contrary to prior online dating and gender literature, findings also indicate a greater willingness of women compared to men to use terms that indicate their body might be overweight. Theoretical explanations look at how positions relative to hegemonic power may be an overriding influence in the importance of body type impression management strategies.
Identifer | oai:union.ndltd.org:ucf.edu/oai:stars.library.ucf.edu:etd-3552 |
Date | 01 January 2013 |
Creators | Latinsky, Andrew |
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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