This study examines public sentiment in Saudi Arabia towards overweight and obesity, focusing on the impact of the COVID-19 pandemic. It explores how social media discourse reinforces weight stigma and affects public perception and behaviour.
Using Twitter API v2, Arabic-language posts geolocated to Saudi Arabia were collected and filtered by relevant keywords. Sentiment analysis was performed using natural language processing and machine learning.
Over 96% of tweets referencing overweight and obesity expressed negative sentiment. Posts linking these terms to COVID-19 or weight gain were similarly unfavourable. Digital stigma may undermine health outcomes and compliance. Value/Originality: The study shows how Artificial Intelligence (AI)-driven sentiment analysis can expose real-time health biases to inform stigma-sensitive policy.
Dialectal variation and linguistic complexity may affect sentiment detection, although dataset size ensured robust classification.
Sentiment analysis supports health surveillance by identifying stigmatising narratives and guiding culturally responsive interventions.
El Kheir, D. Y. M., Alghamdi, R. J., Badoghaish, R. A., AlMurtadha, Y. and Elfaki, A. O. (2025): Overweight and Obesity Stigma within the Saudi Community: what does ‘Sentiment Analysis’ Technique tell us?. In Ahmed, A. (Ed.): United Nations: What Next After 2030 Agenda and SDGs. World Sustainable Development Outlook 2025, Vol. 21, pp. 167-180. WASD: London, United Kingdom.
