OVERWEIGHT AND OBESITY STIGMA WITHIN THE SAUDI COMMUNITY: WHAT DOES 'SENTIMENT ANALYSIS' TECHNIQUE TELL US?

PURPOSE: 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. DESIGN/METHODOLOGY/APPROACH: 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. FINDINGS: 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. ORIGINALITY/VALUE: The study shows how Artificial Intelligence (AI)-driven sentiment analysis can expose real-time health biases to inform stigma-sensitive policy. RESEARCH LIMITATIONS/IMPLICATIONS: Dialectal variation and linguistic complexity may affect sentiment detection, although dataset size ensured robust classification. PRACTICAL IMPLICATIONS: Sentiment analysis supports health surveillance by identifying stigmatising narratives and guiding culturally responsive interventions.