Overweight and Obesity Stigma within the Saudi Community: what does ‘Sentiment Analysis’ Technique tell us?, Dr Dalia El Kheir, Reema Alghamdi, Raghad Badoghaisha, Prof. Yahya AlMurtadha, Dr Abdelrahman Elfaki

Dr Dalia Yahia M. El Kheir
Consultant, Department of Family and Community Medicine
Imam Abdulrahman bin Faisal university
Dammam
Saudi Arabia
ORCID: 0000- 0001- 6033- 1045
Reema Jamaan Alghamdi
Student, College of Medicine
Imam Abdulrahman bin Faisal university
Dammam
Saudi Arabia
ORCID: 0009-0006-6455-0468
Raghad Adel Badoghaisha
Student, College of Medicine
Imam Abdulrahman bin Faisal university
Dammam
Saudi Arabia
ORCID: 0009-0006-1697-1655
Professor Yahya AlMurtadha
Professor, Computer Science institution
University of Tabuk
Tabuk
Saudi Arabia
ORCID: 0000-0003-2171-5470
Dr Abdelrahman Osman Elfaki
Associate Professor, Computer Science institution
University of Tabuk
Tabuk
Saudi Arabia
ORCID: 0000-0002-8881-0504

Purpose: This study examines public sentiment in Saudi Arabia toward 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 behavior.
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 unfavorable. Digital stigma may undermine health outcomes and compliance.
Value/originality: The study shows how 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, though dataset size ensured robust classification.
Practical implications: Sentiment analysis supports health surveillance by identifying stigmatizing narratives and guiding culturally responsive interventions.
Keywords: Sentiment Analysis, Artificial Intelligence Model (AI), Natural Language Processing, Social Media, Twitter, Obesity, Overweight, COVID-19.