Performer Bodily Appearance (Portrayals of Sexuality in Pornography)
Authors: Döring Nicola , Miller Dan J. 
Published: October 2022

Pornography is a fictional media genre that depicts sexual fantasies and explicitly presents naked bodies and sexual activities for the purpose of sexual arousal (Williams, 1989; McKee et al., 2020). Regarding media ethics and media effects, pornography has traditionally been viewed as highly problematic. Pornographic material has been accused of portraying sexuality in unhealthy, morally questionable and often sexist ways, thereby harming performers, audiences, and society at large. In the age of the Internet, pornography has become more diverse, accessible, and widespread than ever (Döring, 2009; Miller et al., 2020). Consequently, the depiction of sexuality in pornography is the focus of a growing number of content analyses of both mass media (e.g., erotic and pornographic novels and movies) and social media (e.g., erotic and pornographic stories, photos and videos shared via online platforms). Typically, pornography’s portrayals of sexuality are examined by measuring the prevalence and frequency of sexual practices or relational dynamics and related gender roles via quantitative content analysis (for research reviews see Carrotte et al., 2020; Miller & McBain, 2022). This entry focuses on the representation of performer bodily appearance as one of eight important dimensions of the portrayals of sexuality in pornography.
 
Field of application/theoretical foundation:
In the field of pornographic media content research, different theories are used, mainly 1) general media effects theories, 2) sexual media effects theories, 3) gender role, feminist and queer theories, 4) sexual fantasy and desire theories, and different 5) mold theories versus mirror theories. The DOCA entry “Conceptual Overview (Portrayals of Sexuality in Pornography)” introduces all these theories and explains their application to pornography. The respective theories are applicable to the analysis of the depiction of performer bodily appearance as one dimension of the portrayals of sexuality in pornography.
 
References/combination with other methods of data collection:
Manual quantitative content analyses of pornographic material can be combined with qualitative (e.g., Keft-Kennedy, 2008) as well as computational (e.g., Seehuus et al., 2019) content analyses. Furthermore, content analyses can be complemented with qualitative interviews and quantitative surveys to investigate perceptions and evaluations of the portrayals of sexuality in pornography among pornography’s creators and performers (e.g., West, 2019) and audiences (e.g., Cowan & Dunn, 1994; Hardy et al., 2022; Paasoonen, 2021; Shor, 2022). Additionally, experimental studies are helpful to measure directly how different dimensions of pornographic portrayals of sexuality are perceived and evaluated by recipients, and if and how these portrayals can affect audiences’ sexuality-related thoughts, feelings, and behaviors (e.g., Kohut & Fisher, 2013; Miller et al., 2019).
 
Example studies for manual quantitative content analyses:
Common research hypotheses state that performers in pornography are mainly selected and presented to conform to gendered norms of sexual attractiveness but also potentially unhealthy beauty standards or current beauty trends. To test such hypotheses and code pornographic material accordingly, it is necessary to clarify the concept of “performer bodily appearance” and use valid and reliable measures for different aspects of appearance. In addition, it is necessary to code the sex/gender of the persons depicted.
Two different approaches to coding are available: Direct coding based on the performer’s appearance (e.g., breast size) versus indirect coding based on meta-information about the material, such as the sub-genre pornography category the material belongs to (e.g., the “big tits”, “BBW” [big beautiful women], “tattoed women” categories on PornHub) or statistics provided as part of performer profiles published on online platforms (e.g., height, weight, bra or penis size).
 

Coding Material

Measure

Operationalization (excerpt)

Reliability

Source

Performer Bodily Appearance: Among the many aspects of performer appearance, those conventionally related to sexual attractiveness are measured most often in the context of pornography research. Researchers may also measure variables related to general beauty trends in society (e.g., shaving of pubic and body hair) or assess aspects of performer appearance which could be consider to promote unhealthy/unrealistic beauty standards (e.g., performers being unhealthily underweight or extremely muscular). Apart from issues of performer health protection, unhealthy standards of beauty and sexual attractiveness are also regarded as relevant in terms of modelling behaviors for audiences.

N=50 best-selling pornographic videos and DVDs in Australia in 2003 with 838 sexual scenes

Performer body type

Performer body type. Polytomous coding (1: unhealthy underweight; 2: slim / undertoned; 3: average (untoned); 4: average (toned); 5: bulked up / very muscular; 6: overweight).

Not available
 

McKee et al. (2008)

Performer breast size

Performer breast size. Polytomous coding (1: smaller than average breasts; 2: average-sized breasts; 3: larger than average breasts).

Performer breast surgery

Performer breast surgery is obvious. Polytomous coding (1: yes; 2: no; 3: unsure).

Performer penis size

Performer penis size. Polytomous coding (1: smaller than average penis; 2: average-sized penis; 3: larger than average penis).

N > 6,900 performer profiles from 10 gay male adult websites

Performer penis size

Performer penis size (as listed in performer profile). Polytomous coding (1: 5–6.5 inches; 2: 7–8 inches, 3: 8.5–10 inches, 4: 10.5–13 inches)

Not available

Brennan (2018)

N=50 MILF [“Mother I’d like to fuck” sub-genre category] and 50 “Teen” pornographic videos randomly selected from 10 different adult websites (10 videos per website)

Performer pubic hair

Performer pubic hair. Polytomous coding (1: none; 2. groomed; 3: natural).

Percentage agreement across all variables in codebook: 90.3%

Vannier et al. (2014)

 
References
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