Problematic Advertising and its Disparate Exposure on Facebook

• URL: http://arxiv.org/abs/2306.06052v1
• Date: Fri, 9 Jun 2023 17:23:59 GMT
• Title: Problematic Advertising and its Disparate Exposure on Facebook
• Authors: Muhammad Ali, Angelica Goetzen, Alan Mislove, Elissa M. Redmiles, Piotr Sapiezynski
• Abstract summary: We study Facebook and investigate key gaps in our understanding of problematic online advertising. We find that older people and minority groups are especially likely to be shown such ads. Given that 22% of problematic ads had no specific targeting from advertisers, we infer that ad delivery algorithms played a significant role in the biased distribution of these ads.
• Score: 15.667983888666312
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Targeted advertising remains an important part of the free web browsing experience, where advertisers' targeting and personalization algorithms together find the most relevant audience for millions of ads every day. However, given the wide use of advertising, this also enables using ads as a vehicle for problematic content, such as scams or clickbait. Recent work that explores people's sentiments toward online ads, and the impacts of these ads on people's online experiences, has found evidence that online ads can indeed be problematic. Further, there is the potential for personalization to aid the delivery of such ads, even when the advertiser targets with low specificity. In this paper, we study Facebook -- one of the internet's largest ad platforms -- and investigate key gaps in our understanding of problematic online advertising: (a) What categories of ads do people find problematic? (b) Are there disparities in the distribution of problematic ads to viewers? and if so, (c) Who is responsible -- advertisers or advertising platforms? To answer these questions, we empirically measure a diverse sample of user experiences with Facebook ads via a 3-month longitudinal panel. We categorize over 32,000 ads collected from this panel ($n=132$); and survey participants' sentiments toward their own ads to identify four categories of problematic ads. Statistically modeling the distribution of problematic ads across demographics, we find that older people and minority groups are especially likely to be shown such ads. Further, given that 22% of problematic ads had no specific targeting from advertisers, we infer that ad delivery algorithms (advertising platforms themselves) played a significant role in the biased distribution of these ads.

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