skip to main content
10.1145/3485447.3512253acmconferencesArticle/Chapter ViewAbstractPublication PageswwwConference Proceedingsconference-collections
research-article

Regulating Online Political Advertising

Published:25 April 2022Publication History

ABSTRACT

In the United States, regulations have been established in the past to oversee political advertising in TV and radio. The laws governing these marketplaces were enacted with the fundamental premise that important political information is provided to voters through advertising, and politicians should be able to easily inform the public. Today, online advertising constitutes a large fraction of all political ad spending, but lawmakers have not been able to keep up with this rapid change. In the online advertising marketplace, ads are typically allocated to the highest bidder through an auction. Auction mechanisms provide benefits to platforms in terms of revenue maximization and automation, but they operate very differently to offline advertising, and existing approaches to regulation cannot be easily implemented in auction-based environments. We first provide a theoretical model and deliver key insights that can be used to regulate online ad auctions for political ads, and analyze the implications of the proposed interventions empirically. We characterize the optimal auction mechanisms where the regulator takes into account both the ad revenues collected and societal objectives (such as the share of ads allocated to politicians, or the prices paid by them). We use bid data generated from Twitter’s political advertising database to analyze the implications of implementing these changes. The results suggest that achieving favorable societal outcomes at a small revenue cost is possible through easily implementable, simple regulatory interventions.

References

  1. Zachary Albert. 2017. Trends in Campaign Financing, 1980-2016. Report for the Campaign Finance Task Force, Bipartisan Policy Center. Retrieved from https://bipartisanpolicy.org/wp-content/uploads/2018/01/Trends-in-Campaign-Financing-1980-2016.-Zachary-Albert.pdf 1, 1 (2017), 22–24.Google ScholarGoogle Scholar
  2. Susan Athey, Dominic Coey, and Jonathan Levin. 2013. Set-asides and subsidies in auctions. American Economic Journal: Microeconomics 5, 1 (2013), 1–27.Google ScholarGoogle ScholarCross RefCross Ref
  3. Alexei Boulatov and Sergei Severinov. 2021. Optimal and efficient mechanisms with asymmetrically budget constrained buyers. Games and Economic Behavior 127 (2021), 155–178.Google ScholarGoogle ScholarCross RefCross Ref
  4. Philip Bump. 2018. Trump’s Facebook advertising advantage, explained. https://www.washingtonpost.com/news/politics/wp/2018/02/27/trumps-facebook-advertising-advantage-explained/.Google ScholarGoogle Scholar
  5. Elisa Celis, Anay Mehrotra, and Nisheeth Vishnoi. 2019. Toward Controlling Discrimination in Online Ad Auctions. In Proceedings of the 36th International Conference on Machine Learning(Proceedings of Machine Learning Research, Vol. 97), Kamalika Chaudhuri and Ruslan Salakhutdinov (Eds.). PMLR, Long Beach, CA, USA, 4456–4465. http://proceedings.mlr.press/v97/mehrotra19a.htmlGoogle ScholarGoogle Scholar
  6. Chapter 5 of Title 47 of the United States Code 315, Subchapter III, Part 1, Section 315. 1934. Candidates for public office. https://www.law.cornell.edu/uscode/text/47/315.Google ScholarGoogle Scholar
  7. Shuchi Chawla and Meena Jagadeesan. 2020. Fairness in ad auctions through inverse proportionality. arxiv:2003.13966 [cs.GT]Google ScholarGoogle Scholar
  8. Court of Appeals District of Columbia Circuit. 1980. Hernstadt v. FCC. Court of Appeals, Dist. of Columbia Circuit, District of Columbia, USA. 893 pages.Google ScholarGoogle Scholar
  9. Laura Edelson, Shikhar Sakhuja, Ratan Dey, and Damon McCoy. 2019. An Analysis of United States Online Political Advertising Transparency. arxiv:1902.04385 [cs.SI]Google ScholarGoogle Scholar
  10. Misyrlena Egkolfopoulou. 2019. Facebook Is Big Winner in Democrats’ 2020 Presidential Debates. https://www.bloombergquint.com/onweb/social-media-ads-could-bleed-some-2020-democratic-candidates-dry.Google ScholarGoogle Scholar
  11. Lodewijk Gelauff, Ashish Goel, Kamesh Munagala, and Sravya Yandamuri. 2020. Advertising for Demographically Fair Outcomes. arxiv:2006.03983 [cs.GT]Google ScholarGoogle Scholar
  12. Matthew D Hoffman and Andrew Gelman. 2014. The No-U-Turn sampler: adaptively setting path lengths in Hamiltonian Monte Carlo.J. Mach. Learn. Res. 15, 1 (2014), 1593–1623.Google ScholarGoogle Scholar
  13. Elena Krasnokutskaya and Katja Seim. 2011. Bid preference programs and participation in highway procurement auctions. American Economic Review 101, 6 (2011), 2653–86.Google ScholarGoogle ScholarCross RefCross Ref
  14. Daniel Kreiss and Matt Perault. 2019. Four Ways to Fix Social Media’s Political Ads Problem — Without Banning Them. https://www.nytimes.com/2019/11/16/opinion/twitter-facebook-political-ads.html.Google ScholarGoogle Scholar
  15. Antonio Garcia Martinez. 2018. How Trump Conquered Facebook—Without Russian Ads. https://www.wired.com/story/how-trump-conquered-facebookwithout-russian-ads/.Google ScholarGoogle Scholar
  16. R Preston McAfee and John McMillan. 1989. Government procurement and international trade. Journal of international economics 26, 3-4 (1989), 291–308.Google ScholarGoogle ScholarCross RefCross Ref
  17. Juan Carlos Medina Serrano, Orestis Papakyriakopoulos, and Simon Hegelich. 2020. Exploring Political Ad Libraries for Online Advertising Transparency: Lessons from Germany and the 2019 European Elections. In International Conference on Social Media and Society (SMSociety ’20). ACM, Toronto, ON, Canada, 111–121. https://doi.org/10.1145/3400806.3400820Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. Roger B Myerson. 1981. Optimal auction design. Mathematics of operations research 6, 1 (1981), 58–73.Google ScholarGoogle Scholar
  19. Milad Nasr and Michael Carl Tschantz. 2020. Bidding strategies with gender nondiscrimination constraints for online ad auctions. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency. ACM, Barcelona, Spain, 337–347.Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. Mallesh M Pai and Rakesh Vohra. 2012. Auction design with fairness concerns: Subsidies vs. set-asides. Technical Report. Discussion Paper.Google ScholarGoogle Scholar
  21. Mallesh M Pai and Rakesh Vohra. 2014. Optimal auctions with financially constrained buyers. Journal of Economic Theory 150 (2014), 383–425.Google ScholarGoogle ScholarCross RefCross Ref
  22. Steve Passwaiter. 2020. Political ad spending this year reached a whopping 8.5 billion. https://adage.com/article/campaign-trail/political-ad-spending-year-reached-whopping-85-billion/2295646.Google ScholarGoogle Scholar
  23. Ellen L Rosen. 1982. Communications Law. Ann. Surv. Am. L. 183(1982), 183.Google ScholarGoogle Scholar
  24. Isaac Stanley-Becker. 2019. Facebook’s ad tools subsidize partisanship, research shows. And campaigns may not even know it.https://www.washingtonpost.com/technology/2019/12/10/facebooks-ad-delivery-system-drives-partisanship-even-if-campaigns-dont-want-it-new-research-shows/.Google ScholarGoogle Scholar
  25. Andrea D Williams. 1992. The Lowest Unit Charge Provision of the Federal Communications Act of 1934, as Amended, and its Role in Maintaining a Democratic Electoral Process. Fed. Comm. LJ 45(1992), 265.Google ScholarGoogle Scholar

Index Terms

  1. Regulating Online Political Advertising
          Index terms have been assigned to the content through auto-classification.

          Recommendations

          Comments

          Login options

          Check if you have access through your login credentials or your institution to get full access on this article.

          Sign in
          • Published in

            cover image ACM Conferences
            WWW '22: Proceedings of the ACM Web Conference 2022
            April 2022
            3764 pages
            ISBN:9781450390965
            DOI:10.1145/3485447

            Copyright © 2022 ACM

            Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

            Publisher

            Association for Computing Machinery

            New York, NY, United States

            Publication History

            • Published: 25 April 2022

            Permissions

            Request permissions about this article.

            Request Permissions

            Check for updates

            Qualifiers

            • research-article
            • Research
            • Refereed limited

            Acceptance Rates

            Overall Acceptance Rate1,899of8,196submissions,23%

          PDF Format

          View or Download as a PDF file.

          PDF

          eReader

          View online with eReader.

          eReader

          HTML Format

          View this article in HTML Format .

          View HTML Format