The Intended and Unintended Consequences of Privacy Protection in Social Media: A Large-Scale Field Experiment and Structural Analysis

Abstract

In the digital age, privacy concerns are escalating with the increased collection and use of personal information. Consequently, regulators have increasingly pushed companies to make the use of personal information more transparent and protect users with more privacy protection measures. However, the impacts of these policies on user behaviors and welfare remain unclear. We investigate this issue through a large-scale, randomized field experiment on a leading global social media platform. In the experiment, treated users were offered a privacy protection option to disable the "People You May Know" (PYMK) recommender algorithm, which could display their content to users whom the algorithm predicts are their friends elsewhere. Control users were neither informed about nor allowed to disable this function. Interestingly, we found that treated users, on average, decreased their video usage time by 0.78% compared with control users. However, the usage time of those treated users who chose to disable the function increased by 17.37% compared with a matched sample. We interpret these results as the privacy protection option having two consequences: On one hand, it raises users' concerns by reminding them that their personal information is being used, thereby unintentionally reducing their usage time. On the other hand, it allows users to disable the use of personal information, which eliminates such concerns and leads to an increase in usage time as intended. To evaluate the social welfare impact of our and alternative privacy protection measures, we estimate a structural model that describes users' decisions regarding usage and disabling the PYMK function, and use the results to run counterfactuals. The results demonstrate that different policies could lead to drastically different social welfare outcomes, highlighting the importance of considering both intended and unintended consequences. In particular, we find that lowering the costs of adopting the PYMK protection option is crucial not only for increasing overall social welfare but also for aligning the incentives of consumers and the platform, potentially creating a win-win scenario for both.

Keywords: Privacy, Privacy Protection, Social Media, Field Experiment, Structural Model