Auctioning Attention on Social Networks

Abstract

Social media recommendation systems create conflict: content producers, content consumers, platform operators, and social pressures struggle to direct the allocation of attention in their favor while also facing competing societal pressures. Producers may feel pressure to optimize for recommendation algorithms and consumers may be exposed to content with negative externalities such as polarization and misinformation. Platforms aim to maximize user engagement, oftentimes resulting in over consumption from consumers. There is mounting pressure from policymakers and broader society to address these issues. Prior methods for constructing social media feeds have focused on recommendation systems. Instead, we propose a, to our knowledge, novel auction based method for feed construction where users bid for the attention of other users. Our mechanism systematically considers producers, consumers, platform operators, and social welfare. We show that our auction is weakly incentive compatible under budget constraints. To balance between producer and consumer welfare, we introduce a tax policy to the auction to increase the cost of content with negative externalities. Simulations over common social network topologies and an empirically observed network show how different feed algorithms prioritize different stakeholders. Our proposed auction based mechanisms produce on average 36.3% higher producer welfare than comparison algorithms on the empirically observed network and 31.4% higher producer welfare than comparison algorithms on the synthetic networks. Our methods also produce more equitable distributions of attention than baseline methods across all evaluated network types. Our proposed mechanism addresses attention allocation at a systematic level, balancing between the needs of different stakeholders.