JOURNAL ARTICLE

Social Media and Deceptive Patterns: A Way Forward for Antitrust Enforcement.

  • Published In: Journal of Competition Law & Economics, 2024, v. 20, n. 4. P. 343 1 of 3

  • Database: Legal Source 2 of 3

  • Authored By: Mattiuzzo, Marcela; Morais, João Carlos Nicolini de 3 of 3

Abstract

This article examines the role of antitrust enforcement in addressing deceptive patterns—defined as manipulative online choice architectures that undermine users' deliberative capacities—deployed by social media platforms and their impact on competition. It reviews existing taxonomies of deceptive patterns from organizations such as the OECD, the UK's Competition and Markets Authority (CMA), the European Commission (EC), and the European Data Protection Board (EDPB), highlighting which categories are relevant to social media's zero-price, advertising-driven business model. The paper argues that deceptive patterns can harm consumer welfare and entrench market power by exploiting behavioral biases, leading to a "race to the bottom" in user engagement strategies, and proposes a two-step antitrust assessment framework: first, determining if a practice impairs consumer decision-making, and second, evaluating whether it strengthens the platform's market power. While acknowledging the importance of complementary regulatory tools like consumer protection and data privacy laws, the article suggests that traditional antitrust enforcement, grounded in the consumer welfare standard, can and should play a role in mitigating competitive harm caused by deceptive patterns in social media and similar digital attention markets.

Additional Information

  • Source:Journal of Competition Law & Economics. 2024/12, Vol. 20, Issue 4, p343
  • Document Type:Article
  • Subject Area:History
  • Publication Date:2024
  • ISSN:17446414
  • DOI:10.1093/joclec/nhae014
  • Accession Number:181970355
  • Copyright Statement:Copyright of Journal of Competition Law & Economics is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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