Privacy and Polarization: An Inference-Based Framework

49 Pages Posted: 2 Jan 2024

See all articles by Tommaso Bondi

Tommaso Bondi

Cornell SC Johnson College of Business

Omid Rafieian

Cornell University - Cornell Tech NYC; Cornell SC Johnson College of Business

Yunfei (Jesse) Yao

The Chinese University of Hong Kong (CUHK)

Date Written: October 30, 2023

Abstract

Advances in behavioral targeting allow news publishers to monetize based on advertising. However, behavioral targeting requires consumer tracking, which has heightened privacy concerns among consumers and regulators. In this paper, we examine how stricter privacy regulations that ban consumer tracking affect news publishers’ content strategies. We develop a theoretical framework that captures a change in privacy policies as a shift in publishers’ inference about consumer types. We consider a model where news publishers choose the content and advertising, and ideologically heterogeneous consumers select their preferred content based on their ideology and idiosyncratic shocks. We compare two salient informational environments: (1) behavioral targeting, where perfect inference about consumers is allowed, and (2) contextual targeting, where consumer tracking is banned due to privacy regulations, and publishers can only infer consumer types based on their content choice. We show that privacy regulations that ban behavioral targeting incentivize publishers to shift towards more extreme and polarizing content in both monopoly and duopoly settings, even though the shift to more extreme content can hurt both demand and consumer welfare. In summary, our research uncovers a previously unexplored relationship between privacy and polarization, shedding light on the potential unintended consequences of privacy regulations in media markets.

Keywords: advertising, targeting, privacy, polarization

JEL Classification: M37, L82, L13, D83

Suggested Citation

Bondi, Tommaso and Rafieian, Omid and Yao, Yunfei (Jesse), Privacy and Polarization: An Inference-Based Framework (October 30, 2023). NET Institute Working Paper No. 23-09, Available at SSRN: https://ssrn.com/abstract=4641822 or http://dx.doi.org/10.2139/ssrn.4641822

Tommaso Bondi (Contact Author)

Cornell SC Johnson College of Business ( email )

Ithaca, NY 14850
United States

Omid Rafieian

Cornell University - Cornell Tech NYC ( email )

2 West Loop Rd.
New York, NY 10044
United States

Cornell SC Johnson College of Business ( email )

Ithaca, NY 14850
United States

Yunfei (Jesse) Yao

The Chinese University of Hong Kong (CUHK) ( email )

Shatin, N.T.
Hong Kong
Hong Kong

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