Building a healthier feed: Private location trace intersection driven feed recommendations

TL;DR

This paper introduces a privacy-preserving method for social media feeds that leverages personal mobility traces to enhance local community integration and social capital, validated through real-world data.

Contribution

It proposes a novel privacy-first approach to using mobility data for social feeds, avoiding third-party exposure and emphasizing real-world social connections.

Findings

Validated approach using existing social-mobility data

Built a reference implementation demonstrating feasibility

Shows potential for privacy-preserving community-focused feeds

Abstract

The physical environment you navigate strongly determines which communities and people matter most to individuals. These effects drive both personal access to opportunities and the social capital of communities, and can often be observed in the personal mobility traces of individuals. Traditional social media feeds underutilize these mobility-based features, or do so in a privacy exploitative manner. Here we propose a consent-first private information sharing paradigm for driving social feeds from users' personal private data, specifically using mobility traces. This approach designs the feed to explicitly optimize for integrating the user into the local community and for social capital building through leveraging mobility trace overlaps as a proxy for existing or potential real-world social connections, creating proportionality between whom a user sees in their feed, and whom the user is likely to see in person. These claims are validated against existing social-mobility data, and a reference implementation of the proposed algorithm is built for demonstration. In total, this work presents a novel technique for designing feeds that represent real offline social connections through private set intersections requiring no third party, or public data exposure.