Does Ad-Free Mean Less Data Collection? An Empirical Study of Platform Data Practices and User Expectations
Sepehr Mousavi, Abhisek Dash, Savvas Zannettou, Krishna P. Gummadi
Abstract
Online platforms increasingly offer ''paid'' ad-free subscriptions as an alternative to the traditional ''free'' ad-based model. The transition to ad-free models ostensibly removes advertising as a key justification for data processing under the GDPR. So, normatively, platforms should collect less user data. However, platforms may justify continued data collection as a means to provide an improved, personalized experience. This tension between privacy principles and platform incentives raises a critical underexplored question: do data collection practices vary between ad-free and ad-based subscription models? In this paper, we shed light on this important privacy issue by investigating the alignment between platform data collection practices and related user expectations. With respect to data collection process, our analyses of data exports from three major online platforms — Instagram, Facebook, and X — reveal that these platforms continue to retain or collect some ad-related data, even in ad-free subscriptions. With respect to user expectations, our survey among 255 participants on Prolific reveals that 69% of the participants normatively expect data collection to be reduced, indicating their expectation of improved digital privacy in an ad-free model. However, when asked what they think actually happens, 63% of these participants believed that platforms would still collect about the same amount of data, highlighting skepticism about platform practices. Our findings not only indicate a significant disconnect between data practices and normative user expectations, but also raise serious questions about platform compliance with core GDPR principles, such as purpose limitation, data minimization, and transparency.
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Your agent calls
Luneget_paper_fulltext
Free to start. No credit card required.
Terminal
Install the CLIlune papers fulltext 222233d4-7ae6-4fef-828f-7df4809fddadBuilds on10
- Oh, the Places You've Been! User Reactions to Longitudinal Transparency About Third-Party Web Tracking and InferencingBen Weinshel, Miranda Wei, Mainack Mondal, Euirim Choi et al.CCS 2019 · 73 citations
- Analyzing User Engagement with TikTok's Short Format Video Recommendations using Data DonationsSavvas Zannettou, Olivia Nemes Nemeth, Oshrat Ayalon, Angelica Goetzen et al.CHI 2024 · 70 citations
- What Makes a "Bad" Ad? User Perceptions of Problematic Online AdvertisingEric Zeng, Tadayoshi Kohno, Franziska RoesnerCHI 2021 · 55 citations
- TikTok and the Art of Personalization: Investigating Exploration and Exploitation on Social Media FeedsKaran Vombatkere, Sepehr Mousavi, Savvas Zannettou, Franziska Roesner et al.WWW 2024 · 45 citations
- Investigating Deceptive Design in GDPR's Legitimate InterestLin Kyi, Sushil Ammanaghatta Shivakumar, Cristiana Teixeira Santos, Franziska Roesner et al.CHI 2023 · 32 citations
Related papers
- Youths' Perceptions of Data Collection in Online Advertising and Social MediaCami Goray, Sarita SchoenebeckCSCW 2022 · 16 citations
- Escaping the Walled Garden? User Perspectives of Control in Data Portability for Social MediaJack Jamieson, Naomi YamashitaCSCW 2023 · 5 citations
- How and Why People Use Virtual Private NetworksAgnieszka Dutkowska-Zuk, Austin Hounsel, Amy Morrill, Andre Xiong et al.USENIX Security 2022
- Setting the Course, but Forgetting to Steer: Analyzing Compliance with GDPR's Right of Access to Data by Instagram, TikTok, and YoutubeSai Keerthana Karnam, Abhisek Dash, Antariksh Das, Sepehr Mousavi et al.S&P 2026 · 3 citations
- Powerful Privacy Norms in Social Network DiscourseNora McDonald, Andrea ForteCSCW 2021 · 28 citations
