Feelings about Bodies: Emotions on Diet and Fitness Forums Reveal Gendered Stereotypes and Body Image Concerns

• URL: http://arxiv.org/abs/2407.03551v1
• Date: Thu, 4 Jul 2024 00:11:27 GMT
• Title: Feelings about Bodies: Emotions on Diet and Fitness Forums Reveal Gendered Stereotypes and Body Image Concerns
• Authors: Cinthia Sánchez, Minh Duc Chu, Zihao He, Rebecca Dorn, Stuart Murray, Kristina Lerman,
• Abstract summary: We analyze 46 Reddit discussion forums related to diet, fitness, and associated mental health challenges. Our findings show that feminine-oriented communities express more negative emotions, particularly in thinness-promoting forums. We also uncover a gendered pattern in emotional indicators of mental health challenges, with communities discussing serious issues aligning more closely with thinness-oriented communities.
• Score: 6.062823012936606
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The gendered expectations about ideal body types can lead to body image concerns, dissatisfaction, and in extreme cases, disordered eating and other psychopathologies across the gender spectrum. While research has focused on pro-anorexia online communities that glorify 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 in online discussions. To address these gaps, we analyze 46 Reddit discussion forums related to diet, fitness, and associated mental health challenges. Using membership structure analysis and transformer-based language models, we project these communities along gender and body ideal axes, revealing complex interactions between gender, body ideals, and emotional expression. Our findings show that feminine-oriented communities generally express more negative emotions, particularly in thinness-promoting forums. Conversely, communities focused on the muscular ideal exhibit less negativity, regardless of gender orientation. We also uncover a gendered pattern in emotional indicators of mental health challenges, with communities discussing serious issues aligning more closely with thinness-oriented, predominantly feminine-leaning communities. By revealing the gendered emotional dynamics of online communities, our findings can inform the development of more effective content moderation approaches that facilitate supportive interactions, while minimizing exposure to potentially harmful content.

Related papers

• Gender Bias in Text-to-Video Generation Models: A case study of Sora [63.064204206220936]
  This study investigates the presence of gender bias in OpenAI's Sora, a text-to-video generation model. We uncover significant evidence of bias by analyzing the generated videos from a diverse set of gender-neutral and stereotypical prompts.
  arXiv  Detail & Related papers  (2024-12-30T18:08:13Z)
• Safe Spaces or Toxic Places? Content Moderation and Social Dynamics of Online Eating Disorder Communities [8.950110714892498]
  Social media platforms have become critical spaces for discussing mental health concerns, including eating disorders. This study addresses this knowledge gap through a comparative analysis of eating disorder discussions across Twitter/X, Reddit, and TikTok. Our findings reveal that while users across all platforms engage similarly in expressing concerns and seeking support, platforms with weaker moderation (like Twitter/X) enable the formation of toxic echo chambers that amplify pro-anorexia rhetoric.
  arXiv  Detail & Related papers  (2024-12-20T09:42:54Z)
• A multitask learning framework for leveraging subjectivity of annotators to identify misogyny [47.175010006458436]
  We propose a multitask learning approach to enhance the performance of the misogyny identification systems. We incorporated diverse perspectives from annotators in our model design, considering gender and age across six profile groups. This research advances content moderation and highlights the importance of embracing diverse perspectives to build effective online moderation systems.
  arXiv  Detail & Related papers  (2024-06-22T15:06:08Z)
• A Holistic Indicator of Polarization to Measure Online Sexism [2.498836880652668]
  The online trend of the manosphere and feminist discourse on social networks requires a holistic measure of the level of sexism in an online community. This indicator is important for policymakers and moderators of online communities. We build a model that can provide a comparable holistic indicator of toxicity targeted toward male and female identity and male and female individuals.
  arXiv  Detail & Related papers  (2024-04-02T18:00:42Z)
• Angry Men, Sad Women: Large Language Models Reflect Gendered Stereotypes in Emotion Attribution [20.21748776472278]
  We investigate whether emotions are gendered, and whether these variations are based on societal stereotypes. We find that all models consistently exhibit gendered emotions, influenced by gender stereotypes. Our study sheds light on the complex societal interplay between language, gender, and emotion.
  arXiv  Detail & Related papers  (2024-03-05T17:04:05Z)
• "I'm fully who I am": Towards Centering Transgender and Non-Binary Voices to Measure Biases in Open Language Generation [69.25368160338043]
  Transgender and non-binary (TGNB) individuals disproportionately experience discrimination and exclusion from daily life. We assess how the social reality surrounding experienced marginalization of TGNB persons contributes to and persists within Open Language Generation. We introduce TANGO, a dataset of template-based real-world text curated from a TGNB-oriented community.
  arXiv  Detail & Related papers  (2023-05-17T04:21:45Z)
• Gendered Mental Health Stigma in Masked Language Models [38.766854150355634]
  We investigate gendered mental health stigma in masked language models. We find that models are consistently more likely to predict female subjects than male in sentences about having a mental health condition.
  arXiv  Detail & Related papers  (2022-10-27T03:09:46Z)
• Gender bias in magazines oriented to men and women: a computational approach [58.720142291102135]
  We compare the content of a women-oriented magazine with that of a men-oriented one, both produced by the same editorial group over a decade. With Topic Modelling techniques we identify the main themes discussed in the magazines and quantify how much the presence of these topics differs between magazines over time. Our results show that the frequency of appearance of the topics Family, Business and Women as sex objects, present an initial bias that tends to disappear over time.
  arXiv  Detail & Related papers  (2020-11-24T14:02:49Z)
• Suicidal Ideation and Mental Disorder Detection with Attentive Relation Networks [43.2802002858859]
  This paper enhances text representation with lexicon-based sentiment scores and latent topics. It proposes using relation networks to detect suicidal ideation and mental disorders with related risk indicators.
  arXiv  Detail & Related papers  (2020-04-16T11:18:55Z)
• Text-mining forma mentis networks reconstruct public perception of the STEM gender gap in social media [0.0]
  Textual forma mentis networks (TFMNs) are glass boxes introduced for extracting, representing and understanding mindsets' structure. TFMNs were applied to the case study of the gender gap in science, which was strongly linked to distorted mindsets by recent studies.
  arXiv  Detail & Related papers  (2020-03-18T13:39:23Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.