Short message service X stigmatizes obesity
The majority of posts on Twitter (today X) convey a negative feeling about being overweight and obese. A Geneva study points to the discrimination spread by social networks.
An analysis of tweets revealed that almost seven out of ten tweets spread negative opinions about overweight and obesity. These are the findings of a study published on Wednesday by the Geneva University Hospitals HUG and the University of Geneva Unige,
The study was conducted in collaboration with researchers from the University of Liverpool and the NHS Greater Manchester Mental Health Foundation. It examined the feelings of the general public, politicians, celebrities and organizations on the subject of obesity based on 53,414 tweets. These were published in English between April 2019 and December 2022, in the middle of the Covid-19 pandemic.
The analysis showed that they were predominantly negative (69 percent) and increasing, while neutral (21 percent) and positive (10 percent) tweets remained stable. Tweets related to obesity were also very frequently associated with racism, other life choices and social phenomena such as the use of illegal substances and alcohol.
The study shows that the negative portrayal of obesity by politicians and celebrities contributes to negative public feelings and the perpetuation of stereotypes and prejudices against overweight and obese people. This stigmatization can have consequences for the mental health and well-being of these people, but also negative effects on public health, the HUG announced on Wednesday.
The role of celebrities
The spikes in criticism correlated with political events and comments from celebrities in the US and UK. For example, many negative messages were spread when overweight celebrities were hospitalized due to Covid-19, when the US president announced his intention to lose weight, or when the UK government launched a campaign against obesity.
When well-known people post negative comments or opinions about obesity, subscribers are more likely to engage in similar conversations, perpetuating the negativity. These results could be useful in the development of health policies, prevention strategies and treatment approaches, the HUG wrote.
The science team used an artificial intelligence-based platform to refine text classification of around 198 million tweets for various applications, including analyzing sentiment in eight different languages. The work was published in the Journal of Medical Internet Research published. (SDA/swi)
