Content analysis of social media regarding obesity as a chronic disease.
PeerJ Comput Sci
; 9: e1321, 2023.
Article
en En
| MEDLINE
| ID: mdl-37346663
Background: Social media is an effective online communication channel. Obesity has been classified as a chronic disease; yet, social media rarely portrays it as such. This study aims to explore the perception of obesity as a chronic disease through content analysis of social media content of obesity-related health organizations and weight loss commercial applications. Methods: Using a codebook adapted from the definition of chronic disease, content analysis was conducted to evaluate a set of posts sampled from 11 health-related organizations and 10 weight loss applications Facebook and Twitter accounts. Descriptive statistics were used to assess the extent obesity was portrayed as a chronic disease. Results: A total of 8,106 posts were extracted: 3,019 posts by organizations and 5,087 by weight loss commercial applications. Only 401 (4.5%) posts/tweets were related to obesity as a chronic disease and were posted by obesity-related health organizations. Only 69 (2.0%) posts from all the organizations' posts directly addressed the idea that obesity is a chronic disease. Almost none of the weight loss commercial apps' social media accounts tackled any aspect of obesity as a disease. Commercial applications' posts revolved mainly around recipes, exercise regimens, and behavioral advice, whereas organizations tackled more complications, treatment, and obesity bias. Conclusion: Using content analysis of social media content, obesity-related health organizations and weight loss applications did not emphasize obesity as a chronic disease on their social media platforms of Facebook and Twitter. Weight-loss commercial applications on social media should include more posts to modify the public's perception regarding obesity as a disease, contributing to health promotion. Further research should explore other social media platforms and posts with specific hashtags posted by the general population.
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Idioma:
En
Revista:
PeerJ Comput Sci
Año:
2023
Tipo del documento:
Article
País de afiliación:
Líbano
Pais de publicación:
Estados Unidos