Violence (Portrayals of Sexuality in Pornography)

Abstract

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 and related gender roles via quantitative content analysis (for research reviews see Carrotte et al., 2020; Miller & McBain, 2022). This DOCA entry focuses on the representation of violence 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 violence 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 pornography depicts sexuality as violent and shows violent acts such as verbal aggression, physical aggression or image-based abuse being predominately perpetrated by men targeting women. To test such hypotheses and code pornographic material accordingly, it is necessary to clarify the concept of “violence” and use valid and reliable measures for different types of violence. In addition, it is necessary to code the sex/gender of the person depicted as the source and/or the target of the respective violent act (e.g., in the context of verbal sexual aggression, the target of verbal aggression is coded as female or male and the source of verbal aggression is coded as female or male).

It is important to note that in the context of pornographic content research, researchers conceptualize violence differently. Also, it should be noted, that there is some overlap between the variable violence and the variable degradation in the context of pornographic portrayals of sexuality. For example, the depiction of “name calling” in a pornographic scene can be understood as an indicator of “violence” (namely verbal aggression) or of “degradation”. Name calling is covered here as verbal aggression (following Fritz et al., 2020), hence, it is not covered again as degradation, even though some authors do so (such as Gorman et al., 2010; see DOCA entry “Degradation (Portrayals of Sexuality in Pornography)”). In general, one can argue that all violent acts – apart from being potentially painful and harmful – have a component of degradation because they put the target of violence in a subordinate role. However, not all degrading acts are violent (e.g., degradation by systematic lack of sexual reciprocity does not entail overt aggression).

Coding Material	Measure	Operationalization (excerpt)	Reliability	Source
Violence: Usually, violence is defined as behavior directed toward the goal of harm or injury of another living being, who is motivated to avoid such treatment (McKee, 2015). However, in content analyses of pornography, violent behavior is often coded regardless of intention to harm or actual harm done. Instead, violence is coded with reference to the presence of prespecified behaviors (e.g., spanking, slapping, choking), even if these behaviors are presented as consensual and sexually arousing (Miller & McBain, 2022). Technology-facilitated sexual violence (image-based sexual abuse) addresses the illegal recording and dissemination of intimate imagery without consent, such as revenge porn, upskirting or spy cams (Henry & Powell, 2018). Mainstream pornography platforms partly disseminate illegal material and partly market some of their legal commercial pornography under these respective labels, hence pretending to provide non-consensual pornography (Vera-Gray et al., 2021). Apart from issues of performer health protection, violent acts are also regarded as relevant in terms of modelling behaviors for audiences.
N=4,009 heterosexual scenes from 3,767 pornographic videos sampled from PornHub.com (574 scenes) and and Xvideos. com (3,435 scenes)	Verbal aggression	“An action that clearly does or could reasonably be expected to cause psychological harm to oneself or another person through name calling or insulting”. Binary coding (1: present; 2: not present).	Percentage Agreement: 97.5% (PornHub) / 88.9% (Xvideos)	Fritz et al. (2020)
	Physical aggression	“Any action that clearly did or could reasonably be expected to cause physical harm to oneself or another person, regardless of the perpetrator’s intent and the target’s response”. Binary coding (1: present; 2: not present).	Percentage Agreement: 98.8% (Pornhub) / 97.6% (Xvideos)
	- Spanking (type of physical aggression)	“Striking on the buttocks with an open hand”. Binary coding (1: present; 2: not present).	Percentage Agreement: 94.2% (Pornhub) / 96.9% (Xvideos)
	- Slapping (type of physical aggression)	“Striking oneself or another with an entirely unclosed hand, group of fingers, or palm”. Binary coding (1: present; 2: not present).	Percentage Agreement: 99.2% (Pornhub) / 98.1% (Xvideos)
	- Gagging (type of physical aggression)	“Any instance in which an object (including the genitals) is inserted into a person’s mouth, such that it appears to cut off their ability to breathe freely and/or causes them to experience a throat spasm”. Binary coding (1: present; 2: not present).	Percentage Agreement: 99.2% (Pornhub) / 96.7% (Xvideos)
	- Pulling hair (type of physical aggression)	“Any instance where the hair on a person’s head is grasped or pulled on, such that the person’s head is pulled (even slightly) in a particular direction”. Binary coding (1: present; 2: not present).	Percentage Agreement: 100.0% (Pornhub) / 98.9% (Xvideos)
	- Choking (type of physical aggression)	“To cause another to stop breathing, if only for a moment, by grabbing the throat”. Binary coding (1: present; 2: not present).	Percentage Agreement: 98.3% (Pornhub) / 98.8% (Xvideos)
	- Pushing (type of physical aggression)	‘‘Use of one’s hands, arms, or other body parts to force another person’s body or part of their body to move in a particular manner or direction”. Binary coding (1: present; 2: not present).	Percentage Agreement: 95.0% (Pornhub) / 97.9% (Xvideos)
N=131,738 titles of pornographic videos presented on the landing pages of the three leading mainstream pornography video platforms in the UK: PornHub.com, Xhamster.com, Xvideos.com	Image-based sexual abuse	Pornographic video title includes keywords indicating image-based sexual abuse such as “spy”, “hidden”, “upskirting”, “leak” or “revenge”. Binary coding (1: present; 2: not present).	Not available	Vera-Gray et al. (2021)

At the same time, porn platforms may disseminate material without the consent of the depicted persons in such violence-indicating categories, but also in regular sub-genre categories (such as "Threesome", "Handjob"), making it impossible for coders to reliably detect all image-based violence.

References

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