2014
DOI: 10.1007/s13142-014-0256-1
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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 Lan… Show more

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Cited by 141 publications
(118 citation statements)
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References 46 publications
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“…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, ).…”
Section: Nature and Extent Of Weight Stigmamentioning
confidence: 99%
“…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, ).…”
Section: Nature and Extent Of Weight Stigmamentioning
confidence: 99%
“…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.…”
Section: Social Identity and Body Imagementioning
confidence: 99%
“…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).…”
mentioning
confidence: 99%