Obesity in social media: a mixed methods analysis

Abstract

The escalating obesity rate in the USA has made obesity prevention a top public health priority. Recent interventions have tapped into the social media (SM) landscape. To leverage SM in obesity prevention, we must understand user-generated discourse surrounding the topic. This study was conducted to describe SM interactions about weight through a mixed methods analysis. Data were collected across 60 days through SM monitoring services, yielding 2.2 million posts. Data were cleaned and coded through Natural Language Processing (NLP) techniques, yielding popular themes and the most retweeted content. Qualitative analyses of selected posts add insight into the nature of the public dialogue and motivations for participation. Twitter represented the most common channel. Twitter and Facebook were dominated by derogatory and misogynist sentiment, pointing to weight stigmatization, whereas blogs and forums contained more nuanced comments. Other themes included humor, education, and positive sentiment countering weight-based stereotypes. This study documented weight-related attitudes and perceptions. This knowledge will inform public health/obesity prevention practice.

Weight Bias and Stigma: Public Health Implications and Structural Solutions

“…The majority of images and video content used in online news portrays individuals with obesity in an unflattering, dehumanizing manner (e.g., headless close‐ups of body parts with ill‐fitting clothing) and engaging in stereotypical unhealthy behaviors (e.g., eating fast food; Heuer, McClure, & Puhl, ; Puhl, Peterson, DePierre, & Luedicke, ). Social media forums such as Twitter and Facebook contain an overwhelming number of “fat jokes” and weight‐derogatory comments, including instances of verbal aggression and cyber‐bullying (Brun, McCarthy, McKenzie, & McGloin, ; Chou, Prestin, & Kunath, ). Additionally, television shows, advertisements, and public health campaigns that focus on weight loss and obesity prevention also contain weight‐stigmatizing content that perpetuates the myth that weight is entirely within an individual's control (Domoff et al., ; Geier, Schwartz, & Brownell, ; Puhl, Luedicke, & Peterson, ; Puhl, Peterson, & Luedicke, ).…”

Thinspiration vs. thicksperation: Comparing pro-anorexic and fat acceptance image posts on a photo-sharing site

“…Overweight individuals can receive support by circulating comments that accept each others' bodies and encourage each other to reject their offline marginalized identities (Smith, Wickes, & Underwood, 2015). For instance, Chou, Prestin and Kunath (2014) found that fat acceptance messages on Twitter were often re-tweeted to enhance positive sentiment.…”

Analyzing big data in social media: Text and network analyses of an eating disorder forum

“…Researchers have employed big data methods in the study of depression support groups on the internet: they studied the language used in the groups (Carron-Arthur, Reynolds, Bennett, Bennett, & Griffiths, 2016;Gkotsis et al, 2017;Xu & Zhang, 2016), predicted users leaving the groups (Sadeque et al, 2016) or showed long-term effects of participation (Park & Conway, 2017). Other health-related issues that have been explored using big data methods include smoking cessation (Zhao et al, 2016), suicidal ideation (De Choudhury & Kiciman, 2017), cancer (Wang, Kraut, & Levine, 2015), HIV/AIDS (Wang, Shi, Chen, & Peng, 2016), and obesity (Chou, Prestin, & Kunath, 2014). A number of systematic reviews have been conducted on all kinds of topics related to research using social media, online communities, and support groups (Carron-Arthur, Ali, Cunningham, & Griffiths, 2015;Eysenbach, Powell, Englesakis, Rizo, & Stern, 2004;Moorhead et al, 2013;Seabrook, Kern, & Rickard, 2016;Sinnenberg et al, 2017;Wongkoblap, Vadillo, & Curcin, 2017).…”