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Leveraging Machine Learning to Identify Gendered Stereotypes and Body Image Concerns on Diet and Fitness Online Forums

Minh Duc Chu, Cinthia Sánchez, Zihao He, Rebecca Dorn, Stuart Murray, Kristina Lerman

TL;DR

This study analyzes 46 Reddit subreddits related to diet, fitness, and mental health to investigate how gendered body ideals (thin vs. muscular) manifest in online discourse. It employs ML tools—node2vec embeddings, transformer-based emotion classifiers, and Fréchet Inception Distance-based content similarity—to map communities along body-ideal and gender axes, and to quantify emotions, toxicity, and structural connectivity. Key findings show thin-ideal spaces are more emotionally expressive and tightly linked to mental health discourse, while muscular-ideal spaces exhibit lower emotionality and more insulated connections from distress communities, with toxicity patterns reflecting both support and hostility depending on context. The work highlights implications for moderation strategies and theory on body image, suggesting avenues for inclusive intervention and better understanding of how gender norms shape online coping and help-seeking behaviors.

Abstract

The pervasive expectations about ideal body types in Western society can lead to body image concerns, dissatisfaction, and in extreme cases, eating disorders and other psychopathologies related to body image. While previous research has focused on online pro-anorexia communities glorifying the "thin ideal," less attention has been given to the broader spectrum of body image concerns or how emerging disorders like muscle dysmorphia ("bigorexia") present on online platforms. To address this gap, we analyze 46 Reddit forums related to diet, fitness, and mental health. We map these communities along gender and body ideal dimensions, revealing distinct patterns of emotional expression and community support. Feminine-oriented communities, especially those endorsing the thin ideal, express higher levels of negative emotions and receive caring comments in response. In contrast, muscular ideal communities display less negativity, regardless of gender orientation, but receive aggressive compliments in response, marked by admiration and toxicity. Mental health discussions align more with thin ideal, feminine-leaning spaces. By uncovering these gendered emotional dynamics, our findings can inform the development of moderation strategies that foster supportive interactions while reducing exposure to harmful content.

Leveraging Machine Learning to Identify Gendered Stereotypes and Body Image Concerns on Diet and Fitness Online Forums

TL;DR

This study analyzes 46 Reddit subreddits related to diet, fitness, and mental health to investigate how gendered body ideals (thin vs. muscular) manifest in online discourse. It employs ML tools—node2vec embeddings, transformer-based emotion classifiers, and Fréchet Inception Distance-based content similarity—to map communities along body-ideal and gender axes, and to quantify emotions, toxicity, and structural connectivity. Key findings show thin-ideal spaces are more emotionally expressive and tightly linked to mental health discourse, while muscular-ideal spaces exhibit lower emotionality and more insulated connections from distress communities, with toxicity patterns reflecting both support and hostility depending on context. The work highlights implications for moderation strategies and theory on body image, suggesting avenues for inclusive intervention and better understanding of how gender norms shape online coping and help-seeking behaviors.

Abstract

The pervasive expectations about ideal body types in Western society can lead to body image concerns, dissatisfaction, and in extreme cases, eating disorders and other psychopathologies related to body image. While previous research has focused on online pro-anorexia communities glorifying the "thin ideal," less attention has been given to the broader spectrum of body image concerns or how emerging disorders like muscle dysmorphia ("bigorexia") present on online platforms. To address this gap, we analyze 46 Reddit forums related to diet, fitness, and mental health. We map these communities along gender and body ideal dimensions, revealing distinct patterns of emotional expression and community support. Feminine-oriented communities, especially those endorsing the thin ideal, express higher levels of negative emotions and receive caring comments in response. In contrast, muscular ideal communities display less negativity, regardless of gender orientation, but receive aggressive compliments in response, marked by admiration and toxicity. Mental health discussions align more with thin ideal, feminine-leaning spaces. By uncovering these gendered emotional dynamics, our findings can inform the development of moderation strategies that foster supportive interactions while reducing exposure to harmful content.
Paper Structure (39 sections, 14 figures, 3 tables)

This paper contains 39 sections, 14 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Sociocultural Mechanisms of Body Image Disturbance
  3. Body Ideals and Social Media
  4. Gendered Emotion and Support in Online Communities
  5. This Study
  6. Methods
  7. Data
  8. Subreddit Mention Network
  9. Mapping Subreddits onto Social Dimensions
  10. Measuring Content Similarity
  11. Measuring Emotions and Toxicity
  12. Emotions
  13. Toxicity
  14. Results
  15. Mapping Subreddits onto the Gender and Body Ideal Spectrum
  16. ...and 24 more sections

Figures (14)

  • Figure 1: Ranking of subreddits along the muscular-thin ideal dimension, measured by cosine similarity. Subreddits on the left discuss the muscular ideal (e.g. r/Getting-Shredded), while communities on the right promote the thin ideal (e.g. r/AnorexiaNervosa). Notably, mental health (non-eating-disorder) subreddits (e.g., r/SuicideWatch) are positioned near the Eating Disorder (ED) communities on the right side.
  • Figure 2: Ranking of our relevant communities along the masculine-feminine dimension.
  • Figure 3: Spearman's correlation coefficient between (left) the body ideal scores and toxicity/emotion scores, and (right) the gender scores and toxicity/emotion scores of different communities in (top) submissions and (bottom) comments. Emotions include the top 4 positive ones (approval, optimism, admiration, and caring) and the top 4 negative ones (disapproval, disappointment, annoyance, and sadness), with the highest median values in submissions and comments, and neutral. The analysis focuses on the top 75% of data with the highest toxic and emotional content. Confidence intervals were obtained by 1000 bootstrap iterations.
  • Figure 4: Distribution of toxicity scores in subreddits, ordered according to the muscular-thin ideal dimension. The bars show the median confidence values of toxicity in submissions (left), comments (middle), and their difference (right), in different subreddits. The analysis focuses on the top 75% of data with the highest toxic content, excluding submissions and comments with a toxicity score below the 25th percentile separately.
  • Figure 5: Network of subreddit mentions. Each point is a subreddit, with edges linking sources to their mentioned subreddits. Node colors represent higher-level clusters, with link colors matching the source subreddit. Node sizes reflect their degrees. The light blue cluster focuses on mental health, dark pink on the keto diet, dark green on body image concerns, purple on extreme diets and eating disorders, and orange and light green on bodybuilding, fitness, and physique goals.
  • ...and 9 more figures