Evaluating AI Models for Food and Alcohol Ad Classification Against Human Raters

Evaluating AI Models for Food and Alcohol Ad Classification Against Human Raters | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Evaluating AI Models for Food and Alcohol Ad Classification Against Human Raters Paula-Alexandra Gitu, Roberto Cerina, Alexander Grigoriev, Stefanie Vandevijvere This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8167519/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 11 Mar, 2026 Read the published version in Scientific Reports → Version 1 posted 16 You are reading this latest preprint version Abstract The growth of food and alcohol marketing on social media creates a need for scalable monitoring methods that go beyond manual processing. This study evaluates whether Large Language Models and Vision-Language Models can recognize advertisements and identify their features in consistence with general public or expert opinion. We collected 1000 Facebook ads from major Belgian brands, and annotated them with 600 crowd workers, three dieticians and four AI models (GPT-4o, Qwen 2.5, Pixtral and Gemma3). Our analysis of the data shows that for single-option advertisement features, like alcohol presence or target group, GPT-4o and Qwen reached agreements with dieticians above 90%, similar to the range of across dieticians. Though agreement was lower for multiple choice features, like premium offers and marketing strategies, it was still within the variability observed in crowd raters. The bias analysis revealed how models interpret certain labels, with some being consistently under- or over-detected. These findings show that AI models can already automate advertisement annotations but still require label modification or expert oversight for some of the features. Health sciences/Health care Physical sciences/Mathematics and computing Biological sciences/Psychology Social science/Psychology Full Text Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterial.pdf Cite Share Download PDF Status: Published Journal Publication published 11 Mar, 2026 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 26 Dec, 2025 Reviews received at journal 25 Dec, 2025 Reviews received at journal 25 Dec, 2025 Reviewers agreed at journal 22 Dec, 2025 Reviews received at journal 21 Dec, 2025 Reviewers agreed at journal 21 Dec, 2025 Reviewers agreed at journal 21 Dec, 2025 Reviews received at journal 20 Dec, 2025 Reviewers agreed at journal 20 Dec, 2025 Reviewers agreed at journal 19 Dec, 2025 Reviewers agreed at journal 19 Dec, 2025 Reviewers invited by journal 08 Dec, 2025 Editor invited by journal 27 Nov, 2025 Editor assigned by journal 27 Nov, 2025 Submission checks completed at journal 26 Nov, 2025 First submitted to journal 26 Nov, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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