Performer Demographics (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 demographics (such as sex/gender, age, and race/ethnicity) 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 demographics as one dimension of 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 in relation to performer demographics state that pornography portrays sexuality in a sexist manner entailing violence towards and degradation of women, usually perpetrated by men. In addition, it is hypothesized that pornographic portrayals of sexuality are asymmetric in terms of showing men in superior and dominating, and women in subordinate and submissive, positions. This sex/gender asymmetry can be reflected in demographic variables such as social status (difference) or age (difference). Furthermore, mainstream pornography is critized for its racist portrayal of sexuality. This means that non-White performers are underrepresented and if they are represented are often depicted according to racial/ethnic stereotypes. To test such hypotheses and code pornographic material accordingly, it is necessary to clarify demographic concepts such as sex/gender, age, and race/ethnicity and use valid and reliable measures.
It is important to note that in the context of pornographic content research, researchers conceptualize demographic characteristics differently and that two different approaches to coding are available: Direct coding based on the person’s appearance (e.g., apparent sex/gender, age or skin color) versus indirect coding based on meta-information about the material, such as the sub-genre category the material belongs to (e.g., pornography category “Asian” displaying Asian-looking performers or “Teen” displaying adult performers who look very young). If applying an intersectional theoretical framework (see DOCA entry “Conceptual Overview (Portrayals of Sexuality in Pornography)”) the researcher would need to code each performer in terms of multiple demographic variables.

Coding Material

Measure

Operationalization (excerpt)

Reliability

Source

Sex/gender: Most analyses of the way sexuality is portrayed in pornography hypothesize (or, at least, acknowledge the possibility) that men and women are depicted differently (e.g., that men are more likely to be depicted as the perpetrators of violent behaviors and that women are more likely to be depicted as recipients of violent behaviors). Accordingly, coding the sex/gender of performers is often essential to addressing research questions in this area. The term gender is often preferred when referring to people as groups, as gender reflects a social categorization, whereas sex reflects a biological categorization (American Psychological Association, 2020). While many content analyses of pornography address sex/gender differences they do not present any standardized measures for the demographic variable of sex/gender. The measure presented below is one of the rare exceptions, but it remains vague in its coding instructions and the meanings of the value “other”.

N=50 segments (length 20 min. each) from a random sample of 50 bestselling pornographic films (1 segment per film) depicting a total of 1,109 sexual behaviors

Sex/gender (based on performer appearance)

“Coder’s perception of character’s sex based on primary and secondary sex characteristics.” Polytomous coding (1: male; 2: female; 3: other).

Cohen’s Kappa: 1.0

Willis et al. (2020)

Age: Performer age may be a variable of interest in its own right (e.g., if investigating whether pornography has a bias toward depicting performers in their early 20s). Alternatively, performer age may be recorded to investigate differential depictions by age group (e.g., investigating whether younger female performers are more likely to be depicted as submissive than older female performers). It should be noted that a performer’s age may be different to their character’s age (as is often the case in the legal pornography category “Teen”, where young characters are played by adult performers; Willis et al, 2020). It should be noted that reliable coding of age (of the performer or of the performed character) is difficult as tools such as make-up, costume, lighting, filters can greatly bias impressions. This problem is reflected in the available measures that cannot ensure sufficient reliability.

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

Age of performer (based on performer appearance)

Performer age. Polytomous coding (1: 18-30 years; 2: 31-40 years; 3: 41-50 years; 4: 51+ years).

Not available

McKee et al. (2008)

N=50 segments (length 20 min. each) from a random sample of 50 bestselling pornographic films (1 segment per film) depicting a total of 1,109 sexual behaviors

Age of character (based on character appearance)

“Coder’s perception of character’s age—not the actor’s—based on physical appearance.” Note: some characters were clearly intended to be under 18 years of age, but their actors were likely older. Polytomous coding (1: <18; 2: 18-20; 3: 21-30; 4: 31-40; 5: 41-50; 6: >50 years).

Cohen’s Kappa: .47

Willis et al. (2020)

Race/Ethnicity: Critical analyses of racism in pornography address the mere visibility of different races/ethnicities as well as racial/ethnic stereotypes, such as Black men being depicted as sexually aggressive and well-endowed or Asian women being depicted as petite, submissive and docile (Miller & McBain, 2022).

N=269 popular pornographic videos from different PornHub.com sub-genre categories

Race/ethnicity (based on pornographic sub-genre category)

Videos selected/coded according to race/ethnicity-related sub-genre categories on PornHub. Polytomous coding (1: “Asian/Japanese” PornHub categeory; 2: “Interracial” PornHub category; 3: “Ebony” PornHub category; 4: “Latina” PornHub category).

Not applicable

Shor & Seida (2019)

N=45 pornographic videos from 15 different adult websites (3 videos per website)

Race/ethnicity (based on performer appearance)

Performers coded according to physical appearance. Binary coding (1: White; 2: non-White/other race).

Not available

Gorman et al. (2010)

N=50 segments (length 20 min. each) from a random sample of 50 bestselling pornographic films (1 segment per film) depicting a total of 1,109 sexual behaviors

Race/ethnicity (based on performer appearance)

“Coder’s perception of character’s race based on physical appearance.” Polytomous coding (1: White; 2: Black; 3: Asian; 4: Latina/o; 5: Native American; 6: other).

Cohen’s Kappa: .94

Willis et al. (2020)

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