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

Dr Dalia Yahia M. El Kheira
Consultant, Department of Family and Community Medicine
Imam abdulrahman bin Faisal university
Dammam
Saudi Arabia
Reema Jamaan Alghamdi
Student, College of Medicine
Imam abdulrahman bin Faisal university
Dammam
Saudi Arabia
Raghad Adel Badoghaisha
Student, College of Medicine
Imam abdulrahman bin Faisal university
Dammam
Saudi Arabia
Professor Yahya AlMurtadha
Professor, Computer Science institution
University of Tabuk
Tabuk
Saudi Arabia
Dr Abdelrahman Osman Elfaki
Associate Professor, Computer Science institution
University of Tabuk
Tabuk
Saudi Arabia

Purpose: This study examines public sentiment in Saudi Arabia toward overweight and obesity, with specific attention to the influence of the COVID-19 pandemic. It explores how social media discourse reflects and reinforces weight stigma, and assesses its potential consequences on public perception and health behaviors.
Design/methodology/approach: Using X (formerly 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 techniques and machine learning algorithms to determine emotional tone.
Findings: Over 96% of tweets referencing overweight and obesity carried negative sentiment. All posts linking these terms to COVID-19 or weight gain were similarly unfavorable. The findings reflect how stigma in digital discourse may undermine public health outcomes and behavioral compliance.
Value/originality: The study highlights the value of AI-based sentiment analysis in capturing public health biases in real time to inform stigma-sensitive policy and intervention strategies.
Research limitations/implications: Dialectal variation and linguistic complexity may influence sentiment detection; however, the dataset’s size supported consistent classification.
Practical implications: Integrating sentiment analysis into public health surveillance may help detect stigmatizing narratives early and guide culturally responsive interventions.
Keywords: Sentiment Analysis, Artificial Intelligence Model (AI), Natural Language Processing, Social Media, X (Twitter), Obesity, Overweight, COVID-19.