Scientists Find a Critical Gap in How Sexist Social Media Content Is Measured

Scroll through almost any social media feed and you will encounter it: memes that joke about women’s place in society, livestreams that reward sexualized self-presentation, influencer content that quietly reinforces old stereotypes about what men and women should be. Researchers have spent years documenting the psychological toll of this material on adolescents and young adults, from self-objectification to body dissatisfaction. But a new systematic review reveals a startling blind spot at the heart of this research field: scientists still lack validated tools to actually measure the gender-discriminatory content itself.

The review, published in Frontiers in Social Psychology by a team at the University of Foggia in Italy, examined how researchers have operationalized exposure to social media content that stereotypes, sexualizes, objectifies, or discriminates against women. Led by Barbara Agueli and Ciro Esposito, the team followed the PRISMA guidelines for systematic reviews, searching Scopus, Web of Science, and PsycINFO for studies published since March 2020, a cutoff chosen because the COVID-19 pandemic dramatically reshaped social media use among young people. After screening 3,574 records with near-perfect inter-rater agreement, only eight studies made the final cut.

That small number is itself a finding. Of the eight studies, just one developed a purpose-built instrument for directly analyzing online content: a Sexualization Coding Scale applied to 1,920 Twitch livestream clips, organized into context and behavior dimensions with scores from 0 to 14, showing strong inter-rater reliability and internal consistency. That study found women streamers were significantly more sexualized than men, particularly in categories like ASMR and Pools/Hot Tubs. Every other study relied on self-reported exposure questionnaires, informal qualitative coding, or ad hoc perceptual scales.

The distinction matters more than it might seem. The review draws on a well-established framework in media research that separates three conceptually distinct levels of exposure. Perceived exposure refers to individuals’ subjective reports of how often they encounter specific content. Actual exposure refers to objectively recorded behavioral traces such as logged platform activity or browsing histories. Content properties refer to the objective characteristics of the material itself, assessed through structured content analysis. These levels capture related but different phenomena, and decades of research show they frequently diverge.

Indeed, previous meta-analytic work has documented substantial discrepancies between what people self-report about their digital media use and what their devices actually log. None of the eight studies in the review measured actual exposure through behavioral or trace data. And only two directly examined content at all, with just one proposing a standardized measurement framework. The result is a literature that knows a great deal about how young people feel about gendered content, and remarkably little about what that content actually contains or how prevalent it is.

The studies that did make it into the review illustrate the pattern. A randomized controlled trial of 150 women tested whether protective filtering strategies could buffer the effects of beauty-ideal Instagram imagery on body image, using validated psychometric scales, but never coded the Instagram content itself. A cross-sectional survey of 6,093 US adolescents linked sexualizing media exposure to sexting behaviors, mediated largely by the belief that sexting is fun, yet exposure was measured entirely through self-report. A Dutch study of 255 sexually active adolescents connected consumption of sexualized posts to sexual coercion perpetration and victimization, moderated by sexual double standards, again without verifying what participants actually saw. A mixed-methods Spanish study associated ambivalent sexism with Instagram and Snapchat use, and a qualitative analysis of 316 choking-related memes across Twitter/X, Instagram, Facebook, and Reddit revealed how such content eroticizes and normalizes potentially dangerous practices, often along gendered lines, but lacked any standardized coding instrument.

Why does this gap persist? The authors point to the sheer difficulty of the task. Social media content is dynamic, heterogeneous, and shaped by platform algorithms that curate different material for different users. Sexism online rarely announces itself; it is embedded in humor, irony, trends, and aestheticized self-presentation, making it less readily recognizable as discriminatory. Platform affordances compound the problem: scalability and persistence let discriminatory content reach vast audiences and remain accessible indefinitely, while recommender systems can amplify sexualized or stereotypical material, and metrics like likes and shares provide social validation that reinforces gendered norms through peer approval.

The stakes are highest for adolescents and young adults, developmental stages marked by heightened sensitivity to peer evaluation, identity formation, and body image concerns. Social media are central contexts where meanings about gender, sexuality, and social value are negotiated during these years. Repeated exposure to stereotypical or objectifying representations has been linked to self-objectification, body surveillance, internalization of gender norms, endorsement of traditional roles, and reduced sensitivity to gender-based violence. Without precise measurement tools, the researchers argue, it remains impossible to determine which specific characteristics of digital content drive these outcomes.

The review’s authors propose a way forward. Future instruments, they argue, should be standardized, theory-driven, and capable of capturing the multidimensional nature of gender discrimination, spanning not only sexualization but also stereotyping, objectification, and subtle normalized forms of sexism. They suggest drawing on the classic tripartite structure of attitudes, measuring cognitive recognition of discriminatory content, affective responses to it, and behavioral tendencies to engage with or share it. They also advocate integrating multiple data sources, combining content analysis with self-reported exposure and behavioral trace data, and incorporating computational methods to scale analysis across rapidly evolving formats like short-form video and livestreaming.

The methodological gap identified here is not merely an academic inconvenience. Interventions designed to protect young people from gender-discriminatory content depend on knowing what that content is, where it circulates, and how much of it users actually encounter. As the authors conclude, advancing the field requires moving beyond an exclusive focus on perceived exposure toward integrated measurement frameworks that link subjective experience, objective exposure patterns, and the structural features of the content itself. Until such tools exist, our picture of the digital environments shaping a generation’s gender attitudes remains, quite literally, a matter of perception.

Subject of Research: Measurement of gender-discriminatory content on social media among adolescents and young adults

Article Title: How is gender-discriminatory content measured on social media? A systematic review revealing a critical gap in measurement tools

Article References: Agueli, B., Sulla, F., Fiorentino, G., Spinelli, A., Merafina, N., Marinelli, C. V., & Esposito, C. (2026). How is gender-discriminatory content measured on social media? A systematic review revealing a critical gap in measurement tools. Frontiers in Social Psychology, 4, Article 1844371. https://doi.org/10.3389/frsps.2026.1844371

Image Credits: AI Generated

DOI: 10.3389/frsps.2026.1844371

Keywords: social media, gender discrimination, sexism, systematic review, measurement tools, adolescents, psychology, content analysis, gender-discriminatory, content, measured, social