Introduction

In the Netflix Series Sex Education we follow the main character Otis Milburn, an average-looking, slim-built teenage boy, as he falls in love and explores his sexuality with various partners. Additionally, the series portrays the sex life of Otis’ mother Jean Milburn an attractive women in her late 40s. By diverging from conventional portrayals of muscular male characters and youthful female figures, both Otis and Jean represent rather unusual demographics in the context of sexual portrayals (Jensen, 2019; Valentine, 2023).

Representation in media, including age, race, face attractiveness, and body type, matters as it shapes societal norms and perceptions, potentially reinforcing or challenging stereotypes and biases (Dixon & Williams, 2015; Ward, 2016). Media may also have a significant influence on the formation of individuals’ expectations regarding sexuality and intimacy (Döring & Miller, 2022a). Who is portrayed in the sexual context inherently conveys societal norms, shaping an implicit narrative of whose sexuality is considered conventional or ‘normal.’ Despite research on pornographic content, there remains limited understanding of how different people and communities are portrayed in the sexual context in mainstream media (Lemke & Tornow, 2018). Consequently, this prompts an investigation into how diverse individuals and their sexualities are represented on leading contemporary media platforms like Netflix.

Netflix has emerged as a dominant digital streaming platform, experiencing a substantial surge in user numbers, reaching approximately 240 million subscribers worldwide in 2023 (Iqbal, 2024). The company has been largely praised for their diverse representations, especially in their in-house productions (Avery, 2023; Stoddart, 2017). For example, a study commissioned by Netflix found a higher percentage of female characters, PoC (People of Color) characters, and non-heterosexual characters in Netflix original content compared to a sample of other top-grossing series or films (Smith et al., 2021). However, recent research has challenged Netflix’s inclusivity claims, pointing to significant underrepresentation of certain demographics like older female characters (Wegner & Stüwe, 2023). The present study aimed to analyze the diversity of characters displayed specifically in heterosexual sexual contexts within highly popular Netflix series.

Whose Sexuality is Shown in Modern Media?

Ageism

Ageism can be described as prejudice or negative misconception against older people, including the perception that they are senile, incompetent, unattractive, dependent, and asexual (Allen & Roberto, 2009; APA, 2018a; Butler, 1969). Especially for women, sexuality is widely associated with youthfulness and vitality (Baber, 2000), often eliminating the visibility and sexual agency of older individuals. This societal bias against aging is also evident in sexual media content, where certain age demographics are disproportionately represented, while depictions of older individuals and sexuality are notably scarce (Gewirtz-Meydan et al., 2018). Recent data from an analysis of nine popular US TV programs found that around one-quarter of sex scenes involved individuals under 25, while the majority of actors were between 26 and 45 years old (Timmermans & Van den Bulck, 2018). Across studies analyzing sexual content in mainstream media, results reveal that characters over the age of 45 were depicted with negligible frequencies of 1% or less (Lacalle & Castro, 2017; Timmermans & Van den Bulck, 2018). An illustrative example comes from a study of French TV series, which examined 171 female actresses, revealing that only 26 (15%) were above the age of 50, while over 40% of the French population is 50 + years old (Arbogast, 2015; Statista, 2024). Out of this group, a mere six (23%) were portrayed as sexually active. In fact, the older female characters were the less likely they were to appear naked on screen (Arbogast, 2015). Similar patterns were found in an analysis of porn, where female performers aged 18 to 30 were portrayed 80% of the time, while those over 40 appeared only 3% of the time (McKee et al., 2008). For male porn performers numbers diverged slightly, with 50% being 18–30 years old and at least 10% over 40 (McKee et al., 2008).

Racism

Racism is defined as a system that assigns opportunity and value based on racial characteristics, leading to disadvantages for marginalized groups, accompanied by prejudice, stereotypes, discrimination, and sometimes violence (APA, 2023). This extends to sexuality where the term sexual racism can be descrives as the “sexual rejection of the racial minority” (Stember, 1978, p. 11). In Western societies, the predominance of White characters in sexual portrayals reflects this deeply rooted racism. For example, in an analysis of nine popular TV series 96% of the depicted characters in sexual encounters were White (Timmermans & Van den Bulck, 2018). Similar results were found in a content analysis looking at sexual scenes in adolescent TV programs where about three-quarters of depicted characters were White (Signorielli & Bievenour, 2015). While pornography includes more PoC characters, they often fall victim to racial stereotypes, with Black men portrayed more aggressively than their White counterparts (Miller & McBain, 2022; for an overview of studies examining age and race representations in pornography, please see Döring & Miller, 2022b).

Lookism

The phenomenon of discriminating against individuals based on their perceived lack of physical attractiveness, known as lookism, highlights a pervasive issue within society (Spiegel, 2023). This perceived lack of attractiveness can be attributed to both body type and face attractiveness, including aspects like facial symmetry, clear skin and hair features, which are critical in societal judgments of physical appeal. This concept is closely related to the narrow beauty standards prevalent in mass media, where a slender body type and conventional face attractiveness, particularly for women, are often emphasized (Slater et al., 2012; Sink & Mastro, 2017). Additionally, white skin is often upheld as a beauty norm, reinforcing racial biases in perceptions of attractiveness (Grabe & Hyde, 2006; Krozer & Gómez, 2023). These beauty ideals are also mirrored in sexual content in mainstream media, where characters are usually attractive, with women outshining men, and female characters commonly portrayed with thin body types (Eyal & Finnerty, 2009; Sink & Mastro, 2017). Results from porn movies match this image, with over 65% of female performers being slim and less than 1% being shown as overweight (McKee et al., 2008, for an overview of studies examining performer bodily appearance in pornography, please see Döring & Miller, 2022c).

Consequences of (Mis)Representation

Exposure to media content significantly influences consumers’ attitudes, beliefs, and behaviors (Donlon et al., 2005; Groesz et al., 2002). Two widely recognized media effects theories, Cultivation Theory (CT; Gerbner, 1998) and Social Cognitive Theory (SCT, originally Social Learning Theory; Bandura, 1971), offer frameworks for understanding these influences. CT posits that prolonged exposure to media content shapes viewers’ perceptions of reality, forming worldviews consistent with the images and messages presented on television. Complementing this, SCT suggests that individuals learn and adopt behaviors by observing others, particularly those portrayed in media.

These theories help explain the formation and reinforcement of stereotypes, generally defined as a “set of cognitive generalizations (e.g., beliefs, expectations) about the qualities and characteristics of the members of a group or social category” (APA, 2018b). The foundation of these stereotypes lies in Social Categorization, a process by which individuals classify themselves and others into distinct social groups based on perceived differences in certain attributes (Tajfel, 1981). Media representations may contribute significantly to this categorization process, shaping societal norms and expectations. This in turn can also affect people’s self-perception. Accordingly, Self-Categorization Theory elaborates on how individuals adopt the identity and norms of the groups they identify with, influencing their self-concept and behaviors to align with group expectations (Turner et al., 1987).

This process can be seen in the context of media influence on body image. For instance, a meta-analysis found that exposure to media images of thin bodies, compared to average or plus-sized bodies, negatively affects people’s body image, particularly among younger participants (Groesz et al., 2002). Similarly, studies on ageism have shown that exposure to media, in general, negatively predicts older people’s perceptions of aging, often due to stereotypical or misrepresented portrayals of older individuals (Donlon et al., 2005; Prieler et al., 2015).

General media effects are likely relevant to sexual media content as well. For example, when mainstream media predominantly showcases slim individuals as sexually active and desirable, it implies that sexual attractiveness and activity are reserved for those who fit this physical criterion. Consequently, individuals who do not see themselves represented as slim may internalize feelings of undesirability, which can affect their confidence and how they express their sexuality. Research indicates that women are particularly vulnerable to societal pressures to conform to gender beauty standards, a trend evident in both pornography and mainstream media (Eyal & Finnerty, 2009; McKee et al., 2008; Ward, 2016).

The Present Research

Although few studies have analyzed demographic variables in sexual contexts such as people’s ages in mainstream media sex scenes (e.g.: Timmermans & Van den Bulck, 2018), none have concentrated on the representation of sexual diversity by conducting an analysis that incorporates multiple demographic variables. More is known about demographic representations of porn actors, however, extending the analysis to everyday media formats is crucial for several reasons. Pornographic content is viewed more commonly by men compared to women (Emmers-Sommer, 2018) and is attributed less realism, limiting its influence on societal perceptions of sexual norms and diversity (Lemke & Tornow, 2018). Sexual portrayals in streaming services like Netflix in comparison might reach broader audiences. Given that exposure to sexual mainstream media content has been found to be related to consumers’ sexual attitudes, beliefs, and behaviors (Alexopoulos & Gamble, 2022; Farrar et al., 2003), investigating demographic representations in sexual portrayals in streaming services might provide a more accurate reflection of societal norms and expectations regarding sexual relationships.

In light of these considerations, the present study aimed to investigate whether the portrayal of characters involved in heterosexual sexual encounters, especially women, is limited to certain demographic profiles within modern Netflix series. This study was pre-registered (https://aspredicted.org/kf98g.pdf).

We made the following prediction: The majority of female characters in sexual scenes conform to classic stereotypical demographics and female beauty standards (young, White, attractive, slim body shape).

Method

We performed a quantitative media content analysis. The focus of this analysis was on N = 91 characters featured in sexual scenes across seven popular Netflix shows. For this study, we coded all character’s present in mixed-gender sexual scenes from all seasons and episodes available for the series as of October 2022. The codebook, data set, and analysis script of the main analysis can be found on the OSF: https://osf.io/xe4sh/?view_only=f32dea2bd8284452ac98c5119ccf9921.

Sampling

Initially, series were included in our analysisthat were chosen based on three inclusion criteria. First, the series had to be available on Netflix in October 2022. Second, we focused on recent content, including only series launched within the past five years (2017 or later). Third, to ensure the popularity and impact of the series, they had to rank among the 30 most-watched series on Netflix in 2022, each accumulating at least 200 million total hours viewed within the first 28 days after release (Solsman, 2023). Upon an initial scan through the Top 30 series, we observed that most did not include explicit sexual scenes, which were of particular interest to our study. Previous research has used Netflix labels to identify series with specific content such as teen-related themes (e.g., Masterson & Messina, 2023). However, Netflix does not provide labels specifically for sexual content. To address this, we used six diverse internet rankings of “sexiest” or “steamiest” Netflix shows to identify series with sexual content, as applied in past content analyses (e.g., Chakrabartty & Mitra, 2022; Colbert, 2022; Ellis & Zane, 2022; Foutch, 2022; Tauty et al., 2021; Vincentry & Pasternak, 2021; Wallace et al., 2022). We chose the most accessed rankings. The six rankings chosen were sourced from diverse websites such as Cosmopolitan and Men’s Health and were selected to ensure a comprehensive and varied representation of audience preferences. Our fourth criterion for series selection was that the series needed to be mentioned in at least three of the six ranking. The initial application of the four criteria resulted in a sample comprising six Netflix-produced series. However, upon review, it was noted that one of these series, namely The Witcher, featured a small number of visual sexual scenes. To ensure a robust sample size and wider representation of characters, we slightly deviated from our pre-registered criteria by including a seventh series, which was mentioned in four of the internet rankings, Sex/Life. We chose to include this series because it was mentioned the most frequently out of all the others listed in the rankings. Additionally, despite not being among the top 30 most-watched series, it has received comparable media attention. (Andreeva, 2021; Television Stats, 2023). The final sample included the following series: Bridgerton (2020), Dark Desire (2020), Élite (2018), Sex Education (2019), Sex/Life (2021), The Witcher (2019), and You (2018). Among these, four series originate from the United States, one is British, one is Mexican, and one is Spanish. For an overview of the series characteristics please see the OSF: https://osf.io/y9mz5?view_only=f32dea2bd8284452ac98c5119ccf9921.

Units of Analysis

Our units of analysis were all characters appearing in mixed-gender sexual scenes in every episode of the selected series available as of October 2022. For each of the characters we coded a set of demographic variables namely age, race, face attractiveness, and body type. Sexual scenes were defined as any scenes where two characters were involved in at least one of 11 sexual behaviors: (1) intimate touching, (2) female self-stimulation, (3) male self-stimulation, (4) manual stimulation of female genitals by man, (5) manual stimulation of male genitals by woman, (6) cunnilingus, (7) fellatio, (8) anilingus, (9) penile-vaginal penetration, (10) anal sex or (11) sex toy use.

A character was coded once they appeared in a mixed-gender sexual scene lasting at least 5 s. Scenes that lasted only 5 s or less were categorized as short scenes. If the character exclusively appeared in a short scene, we did not assign character codes because of limited available information. Due to this approach, we did not encounter any missing data. During the course of the series, in a few instances, characters changed category affiliation in at least one of the codes (e.g., character was shown as both a teenager and an adult). In such cases, we treated each unique combination of codes as a distinct character for coding purposes. Further, we only coded characters we presumed to be cis-gendered women and men. Because we were interested in the comparison between women and men, characters who explicitly stated having other gender identities (e.g. trans* or non-binary) were not included in this analysis.

Content Categories

Age

For age classification, we employed six distinct categories: (1) preteen (ten − 13 years), (2) teen (14–20 years), (3) young adult (21–30 years), (4) adult (31–40 years), (5) middle-aged adult (41–50 years), and (6) older adult (51 years or older). If available, direct information of character’s age was used for categorization (e.g.: the characters age was mentioned in series, context information such as school grade was given). If age information was not provided, we estimated it based on the character’s appearance. In cases of uncertainty between two age categories, the actor’s real age was used to resolve the ambiguity.

Race

Due to the dominance of US-produced series in our sample the conceptualization of race was aligned with Jozkowski and colleagues’ approach (2019). According to the US Census, the following categories were used: (1) Black, (2) White, (3) Hispanic, (4) Asian, and (5) Bi-/Multiracial. For a more comprehensive analysis we added the categories (6) Middle Eastern/Arabic and (7) Native American. Characters were classified based on the category that best matched their observable physical traits and their portrayal within the series (i.e., how viewers would most likely perceive them according to the coders’ evaluation).

Face Attractiveness

For character’s physical attractiveness, we adopted a definition by Eyal and Finnerty (2009, p. 155) where “an attractive character was considered well groomed, pleasing to look at, and healthy-looking” and “an unattractive character had unbecoming or disfigured features, such as poor hair or skin”. Coders assessed whether the average viewer would perceive each character as attractive or not and used the societal average as a point of reference. This variable had three categories: (1) physically attractive, (2) average, and (3) physically unattractive. Importantly, physical attractiveness was coded based on facial and hair features and was scored independently of body build and shape and thus is referred to as “face attractiveness”.

Body-Type

Regarding body type we initially aimed to categorize all characters based on both body type (ranging from ectomorphic to endomorphic) and muscle tone. However, during the training and preliminary coding process, we encountered challenges due to insufficient visible footage to accurately determine muscle tone for many characters, coupled with difficulties in distinguishing between body type and muscle tone. Consequently, we decided to focus solely on coding body type. For women, we coded three body types: (1) ectomorphic, (2) mesomorphic, and (3) endomorphic, following the classifications by Wasylkiw et al. (2009). An ectomorphic body type was defined as a skinny or lean body below the societal average weight. A mesomorphic body type was coded for characters with average body shapes and an average level of visible body fat. An endomorphic body type was assigned to characters with soft and round bodies that are above average weight. For men, we applied these three categories and added a fourth, muscular, to better capture the diversity of male physiques observed. This addition was necessary to distinguish between the mesomorphic (average but not notably muscular) and distinctly muscular body types. A muscular body type is also linked to beauty standards associated with masculinity (Hargreaves & Tiggemann, 2009).

Data Collection and Coding

Before the coding phase, the first author and three student research assistants pre-screened all seven series and seasons to identify explicit sexual scenes. Following scene collection, the first author and another student research assistant (third author) underwent detailed coding training, which was held weekly over the course of six weeks and included practice coding with alternative series (e.g., Sexify, She’s Gotta Have it, Tiny Pretty Things).

Following the training phase the codebook was refined and intercoder reliability was calculated with the first 10 scenes of each series. As a result, 64 scenes were coded, involving 44 characters, constituting 48% of the final sample of characters. This exceeds the recommendations for content analysis, which typically suggest coding 10–20% of the total sample (Neuendorf, 2002).

Cohen’s kappa and Krippendorff’s alpha were used to calculate intercoder reliability. A threshold of 0.80 for both coefficients is considered as substantial or strong agreement (McHugh, 2012; Neuendorf, 2002). This criterion was met for all of our variables (see Table 1).

Table 1 Intercoder Reliability
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Statistical Analysis

For statistical analysis, we used Chi-Square Goodness-of-Fit Tests and Fisher’s Exact Tests. We initially applied Chi-Square Goodness-of-Fit Tests to examine the distribution of each demographic variable within each gender group. These tests evaluated whether observed frequencies significantly differed from expected frequencies, assuming equal distribution across categories. This method is suitable when expected frequencies are sufficiently large (> 5 in each cell), a criterion met for all of these calculations.

Additionally, for each content category, we conducted Fisher’s exact test to examine gender differences in the distributions. This method was selected over Chi-square analysis because, unlike Chi-square, Fisher’s exact test is suitable when over 20% of the cells in a contingency table have expected frequencies below five (Kim, 2017). This condition applied to all gender comparison analyses.

Results

We coded a total of 91 characters, including 45 female and 46 male characters. The characters’ demographics and comparisons between female and male characters are presented in Table 2.

Table 2 Character Demographics and Gender Comparisons
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Main Analysis

Female Characters

The Chi-Square Goodness-of-Fit Test indicated a significant difference in the distribution of age categories among female characters, χ²(4, N = 45) = 23.78, p < .001, compared to the expected distribution. There was a significant underrepresentation of middle-aged and older women, while the teen, young adult and adult categories were overrepresented. Specifically, less than 10% of female characters were 40+. For the coding of character’s race, again the observed distribution significantly deviated from the expected distribution, χ²(5, N = 45) = 32.20, p < .001, with racial minority groups being underrepresented compared to White characters. Moreover, there was a significant underrepresentation of average looking and physically unattractive women, compared to physically attractive female characters, χ²(2, N = 45) = 32.93, p < .001. Lastly, for women mesomorphic body types were largely underrepresented compared to ectomorphic body types, while endomorphic body types were completely absent, χ²(1, N = 45) = 33.80, p < .001.

Male Characters

For men the Chi-Square Goodness-of-Fit Test regarding age distribution yielded a significant result, χ²(4, N = 46) = 19.65, p < .001, with a slight underrepresentation of young adults and a larger underrepresentation of men aged 40+, compared to teen and adult characters. Again, racial minority groups were underrepresented compared to White characters, χ²(4, N = 45) = 62.91, p < .001. Regarding male characters’ face attractiveness, physically unattractive characters were underrepresented compared to physically attractive and average characters, χ²(2, N = 45) = 16.00, p < .001. In a similar vein, ectomorphic and endomorphic men were underrepresented, while muscular built men were largely overrepresented, χ²(3, N = 45) = 42.52, p < .001.

Gender Comparison

For both genders regarding characters’ age, we found predominately young people being featured in explicit sexual scenes. However, Table 3 shows that women were more likely to fall into the young adult category compared to men, and men were more often represented in the middle-aged or older adult categories. Also, our results showed a large proportion of teenage characters, due to the inclusion of two explicit teen-oriented series (Sex Education and Élite). A subsequent analysis was conducted after excluding these series from the dataset. The results marked a high prevalence of women in young adult category (54%), with the remaining divided between teenagers and adults. For men the majority were adults (52%), while the rest were teens, young adults, or middle-aged adults. Fisher’s exact test comparing age distribution across genders did not reveal significant differences, p = .20.

Table 3 Distribution of Age Categories Across Genders
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In terms of race, the largest group of characters, both women and men, were White. However, for women, more than half of the identified characters were Hispanic, Black, or Asian, while for men only about a third of the characters were not White. No significant difference between women and men was found. Please see Table 4 for the race distributions across both genders. A notable example of racial diversity in sexual representation can be seen in the series Bridgerton, which features two diverse main couples across its two seasons. In the first season, Daphne Bridgerton, who is White, explores her sexuality with Simon Basset, a Black character. The second season portrays the romantic journey of Kate Sharma, a South-Asian character, with Anthony Bridgerton, who is White.

Table 4 Distribution of Race Categories Across Genders
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We did find a significant gender difference in face attractiveness, with female characters more likely to be physically attractive compared to male characters (please also see Table 5). To exemplify, our analysis identified a solitary instance of a female character depicted as unattractive, specifically Yennefer vonVengerberg from The Witcher. Yennefer, who is a half-elf, is initially presented with congenital deformities affecting her spine and jaw. Subsequently, through the intervention of a magical ritual, her appearance is dramatically altered, resulting in conventionally attractive features including facial symmetry, enlarged brown eyes, and pronounced lips.

Table 5 Distribution of Face Attractiveness Categories Across Genders
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Furthermore, women were predominantly portrayed with ectomorphic body types, emphasizing slimness, matching conventional female beauty standards. On the other hand, men were most often presented with muscular physique, aligning with male beauty standards.

Exploratory Analysis

To gain a nuanced understanding of the interplay between various demographic variables, we conducted Fisher’s exact tests for all possible combinations (e.g., race x attractiveness) separately for women and men (please see the OSF for a table with a full overview of all calculations: https://osf.io/39ydz/?view_only=70d0613af67142e5826963e8f487c9be). For female characters, two significant intersection were observed. First, the association between age and attractiveness was significant applying Fisher’s exact test, p = .038, V = 0.367. The data reveals that individuals in the teen and older adult categories were observed to be physically attractive less frequently than expected, while young adults and adults were observed to be physically attractive more often than expected. Second, Fisher’s exact test between age and body type showed a significant association between the variables, p = .003, V = 0.814. The analysis uncovered that the ectomorphic body type was overwhelmingly represented across different age categories, except in the older adult group, which predominantly featured characters with mesomorphic, average bodies. To illustrate, only two female characters portrayed in sexual scenes that are above the age of 50, Maureen Groff and Cynthia Jenkins, from Sex Education, were both among the only three female characters in total presented with a mesomorphic and not an ectomorphic body type.

For male characters, we found a significant relationship between age and face attractiveness, p = .014, V = 0.421. Young adult and adult characters were more frequently categorized as physically attractive than expected, while middle-aged adults and older adults were less likely to be physically attractive. Also, there was a significant association between body-type and face attractiveness, p < .001, V = 0.65, revealing that muscular characters predominantly fell into the physically attractive category, while overweight individuals were exclusively viewed as physically unattractive.

Discussion

In the present research we aimed to analyze who is represented in sexual portrayals in selected contemporary Netflix shows. Most characters depicted in sexual contexts were young, attractive, either slim or with muscular bodies. Notably, this tendency towards stereotypical imagery was slightly more evident in female characters. Female characters were less frequently aged 40 + and more often depicted as attractive compared to their male counterparts. Regarding race, we found substantial diversity in representations.

Thereby, the present results reflect power dynamics inherent in society and media production (i.e., ageism, racism, lookism, and sexism), which inevitably shape the representation of characters in mainstream TV. Thus, it is not surprising that in our sample of sexually active characters there are few older individuals, fewer racial minorities, fewer average or plus-size individuals, and fewer conventionally unattractive characters. Despite a growing recognition of diverse body ideals and the importance of representing various age groups and races, the media industry confronts substantial challenges in transforming ingrained standards, demanding sustained efforts from content creators, industry leaders, and audiences.

Age

In our analysis, we observed a notable underrepresentation of individuals aged 40 +, affecting both genders, with a more pronounced discrepancy for women. This matches the general underrepresentation of older women in TV (Wegner & Stüwe, 2023) and supports research suggesting that this underrepresentation becomes even more accentuated in the context of sexuality (Arbogast, 2015). Adults aged 40 +, particularly women, are often portrayed as lacking sexual appeal, reinforcing the stereotype of them being non-sexual beings (Gewirtz-Meydan et al., 2018; Kessler et al., 2004). The prevalent narrative reflects a societal discomfort and reluctance to acknowledge and embrace sexual agency of older people.

Contrasting media representations, global trends and research suggest that we are living longer, healthier lives, with increasing evidence that sexual desires and activities persist well into older age, challenging stereotypes of older adults as asexual (Lindau et al., 2007). Despite these realities, the gap in the representation of people aged 40 + in sexual media aligns with real-world consequences, where sexual and reproductive health needs of older adults, particularly those beyond reproductive years, remain largely neglected in research, policy, and health programs (Heidari, 2016). Furthermore, older adults often feel hesitant to discuss their sexual concerns with healthcare providers and caregivers (Gott & Hinchliff, 2003). This reluctance may be influenced by the lack of representation of older people’s sexualities in society at large. The absence of media portrayals of older people’s sexuality might also shape younger viewers’ perceptions of aging and sexuality. Research indicates that young people often believe that as they age, they will become less interested in diverse sexual activities, engage in less sex, and face more sexual challenges (Floyd & Weiss, 2001; Lai & Hynie, 2011). Increasing the visibility of older adults’ sexualities in mainstream media might help normalize their sexual experiences, influencing both older and younger viewers’ perceptions of aging and sexuality.

Race

In contrast to our expectation of a great majority of White characters, our study highlighted substantial racial diversity among characters in sexual contexts in the selected Netflix series. Approximately every second female character and every third male character depicted was of a not White. This may indicate a positive trend, in line with studies indicating that platforms like Netflix are somewhat more racially diverse compared to other mainstream media sources (Smith et al., 2021; Wegner & Stüwe, 2023).

Beauty Ideals

We found a lack of diversity in characters deviating from conventional beauty standards regarding their face attractiveness and body-types, especially among women who were portrayed in the sexual realm. The great majority of female characters in our sample were slim or skinny, while a significant portion of male characters exhibited muscular builds. While male body representations were slightly more varied, overweight portrayals were virtually absent for both genders. These results echo Wegner and Strüwe’s (2023) findings on general streaming platform content, where 98% of female characters in mainstream series had thin bodies compared to more diverse physiques for male characters.

Our results are crucial since media representation might shape ideas about societal expectations for “sexual bodies” (Montemurro & Gillen, 2013). The exclusion of individuals with average or larger body sizes or those considered less conventionally attractive in terms of their facial features is concerning, as it insinuates that these bodies are not sexually desirable (Hall, 2018). Studies show that people rate larger bodies as less sexually attractive and tend to associate them more frequently with traits suggestive of sexual inhibition (such as being sexually repressed) and less often with traits indicative of sexual confidence and experience (such as seductiveness and sexual expertise) compared to skinny or average-sized bodies (Oswald et al., 2022). Objectification theory elucidates how such narrow media portrayals can lead especially women to internalize an observer’s perspective of their bodies, viewing themselves as objects valued primarily for their appearance (Fredrickson & Roberts, 1997). This internalization may contribute to heightened body dissatisfaction and body image concerns among women (Frederick et al., 2022; Grabe et al., 2008), and even more favorable attitudes towards surgical body modifications (Harrison, 2003). Hence, media portrayals like Netflix may add to societal stereotypes of a sexually desirable female body being young and skinny also in the domain of sexuality (Montemurro & Gillen, 2013).

Limitations and Future Directions

Firstly, the scope of our study was limited to a select number of Netflix series, chosen for their sexual content and popularity; therefore, the findings should be considered within this context. While this targeted approach provides initial insights, future research could broaden the demographic analysis of sexual portrayals to include a more extensive array of series or extend the inquiry to other media platforms and streaming services for a more comprehensive understanding.

Another notable limitation of this study is its exclusive focus on characters in heterosexual interactions within the selected Netflix series. This decision was influenced by the observation that roughly only every tenth sexual scene in the analyzed series involved same-gender sexual encounters (e.g., two women having sex), leading us to concentrate our analysis on mixed-gender encounters. Previous content analyses on depictions of lesbian, gay, or bisexual sex (e.g., Bond, 2015) are available, however, comprehensive studies examining the demographics of characters involved in these non-heteronormative interactions are, to our knowledge, lacking. This gap highlights a significant area for future research. By expanding the scope to include a wider range of sexual orientations and relationships, subsequent studies could provide a more inclusive understanding of people’s sexual representation in media.

There are also limits tothe study in our approach to coding gender, wherein we assumed characters to be cis-gendered. The decision to code gender and not sex was deliberate, as viewers are usually unaware of people’s biological sex (e.g., their chromosomes or hormone levels) but rather infer a certain perceived gender based on observable characteristics (e.g., clothes, hair, voice). This decision was informed by our intention to analyze information that is immediately visible to the viewer, focusing on aspects of representation that can be discerned without knowledge of characters’ internal identities. However, this approach may have overlooked the potential presence of trans* or non-binary characters whose identities might not be explicitly disclosed or recognized based on observable cues alone.

Lastly it is important to acknowledge the limitations of our analysis regarding character demographics, as it does not encompass all relevant variables of representation. For example, diversity characteristics such as disabilities, social class, and more specific aspects related to ethnic background like skin tone among PoC are absent from our study but may play a significant role in fostering feelings of inclusion (Maxwell et al., 2015). Therefore, these aspects merit consideration in future research endeavors. Moreover, our coding team included two white cis women of similar ages due to availability constraints. Future research could possibly benefit from a more heterogeneous coding team providing a wider range of perspectives and reducing potential biases (Jozkowski et al., 2019).

There are several avenues for future research that could extend this study. An initial crucial step would be to advance from mere descriptive analysis of the demographics represented in sexual contexts to a nuanced investigation of how these specific demographics are portrayed. For instance, research on pornographic films has found that Black men are often depicted as more sexually aggressive than White men (Miller & McBain, 2022) and older sexually active women, often known as “cougar” or “MILF” (acronym for “Mother I’d like to fuck”), are generally portrayed as more sexually agentic than younger women (Vannier et al., 2014). This suggests that while there may be more diverse representations of people in sexual media content, these portrayals might still reinforce stereotypes.

Second, while our study helps to understand sexual media representations, we are unable to make any interpretations as to how these representations influence viewers’ actual behavior and attitudes. Therefore, future research would greatly benefit from longitudinal experimental studies investigating the effects of sexual media representations especially on the self-esteem and attitudes of (minority) individuals regarding sexuality. At present, we can only infer these effects from general media studies.

Third, in the present study, we focused on analyzing sexual representations in Netflix shows primarily featuring US content. Cultural and contextual factors, such as regional norms, industry practices, and audience preferences, might significantly influence representations of sexuality and diversity in media. Exploring these dynamics across different cultural contexts (e.g., Bollywood, Latin American telenovelas), where, for example, different ideals of body types exist (Bozsik et al., 2018; Neumark-Sztainer et al., 2002), would be an interesting research endeavor that could contribute to a more nuanced understanding of sexual media portrayal and its societal impacts.

A last relevant point to consider when interpreting our results is the demographic makeup of the individuals responsible for creating and producing media content. Analysis of behind-the-camera personnel, including directors, writers, and producers of Netflix’s self-produced series, reveals that only roughly one-third are women and only 15% are from underrepresented racial groups (Smith et al., 2021). Research demonstrates that directors from underrepresented racial backgrounds are more inclined to produce more diverse media content (Ramos, 2024). Hence, the lack of diversity among content creators of Netflix series likely contributes to the limited diversity representation seen on screen. Future research could examine how increasing diversity among content creators might influence the portrayal of sexuality in media.

Conclusion

Our results show that there is a majority of people widely excluded from representations of sexual interactions in our sample of modern Netflix series. While we did find racial diversity in sexual contexts, the underrepresentation of individuals aged 40 + and those with non-thin/non-muscular body types in media’s sexual narratives could engender feelings of exclusion among these demographics. This pattern hints at a gap in the media’s inclusivity efforts. While there appears to be an awareness and an evident commitment to racial diversity, other crucial dimensions such as age and body type diversity may not be receiving the same level of attention. Additionally, this prompts a critical examination of other potentially overlooked areas in sexual representations for future research, such as the visibility of individuals with disabilities. In sum, our findings underscore the ongoing importance of future research addressing these issues of representation and diversifying sexual media content to reflect the realities and diversity of human sexuality.