SICLCYJul 4, 2024

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

arXiv:2407.03551v23 citationsh-index: 17
Originality Incremental advance
AI Analysis

This addresses body image issues in online forums, offering insights for moderation strategies to reduce harmful content, though it is incremental by extending prior research on pro-anorexia communities to broader concerns like muscle dysmorphia.

The study analyzed 46 Reddit forums to identify gendered stereotypes and body image concerns, revealing that feminine-oriented communities endorsing the thin ideal express higher negative emotions and receive caring comments, while muscular ideal communities show less negativity but receive aggressive compliments.

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.

Foundations

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