• Junjie Lu, Sook Ning Chua, Jill R Kavanaugh, Jaanak Prashar, Egbe Ndip-Agbor, Monique Santoso, Destiny A Jackson, Payal Chakraborty, Amanda Raffoul, and S Bryn Austin.
• Department of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health, Boston, Massachusetts; Department of Epidemiology and Population Health, Stanford University School of Medicine, Stanford, California. Electronic address: junjielu@stanford.edu.
• Am J Prev Med. 2024 Aug 30.

IntroductionStarting June 30, 2022, Google implemented its revised Inappropriate Content Advertising Policy, targeting discriminatory skin-lightening ads that suggest superiority of certain skin shades. This study evaluates the ad content changes from 2 weeks before to 2 weeks after the policy's enforcement.MethodsText ads from Google searches in eight countries (Bahamas, Germany, India, Malaysia, Mexico, South Africa, United Arab Emirates, and United States) were collected in 2022, totaling 1,974 prepolicy and 3,262 post-policy ads, and analyzed in 2023. A gold standard database was established by two coders who labeled 707 ads, which trained five natural language processing models to label the ads, covering content and target demographics. The descriptive statistics and multivariable logistic models were applied to analyze content before versus after policy implementation, both globally and by country.ResultsVertex AI emerged as the best natural language processing model with the highest F1 score of 0.87. There were significant decreases from pre- to post-policy implementation in the prevalence of labels of "Racial or Ethnic Identification" and "Ingredients: Natural" by 47% and 66%, respectively. Notable differences were identified from pre- to post-policy implementation in India, Mexico, and Germany.ConclusionsThe study observed changes in skin-lightening product advertisement labels from pre- to post-policy implementation, both globally and within countries. Considering the influence of digital advertising on colorist norms, assessing digital ad policy changes is crucial for public health surveillance. This study presents a computational method to help monitor digital platform policies for consumer product advertisements that affect public health.Copyright © 2024 Elsevier Inc. All rights reserved.

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