Executive Summary The problem Algorithmic ranking and recommendation systems determine what kinds of behaviors are rewarded by digital platforms like Facebook, YouTube, and TikTok by choosing what content to show to users. Because these platforms dominate our attention economy, and because attention can be transformed into money and power, platform recommendations therefore provide a reward structure for society at large. Platforms currently reward divisive behavior with attention due to the interactions between engagement-based ranking and human psychology. This helps determine the kinds of politicians, journalists, entertainers, and others who can succeed in their respective social arenas, resulting in significant impacts on the quality of our decision-making, our capacity to cooperate, the likelihood of violent conflict, and the robustness of democracy.
The opportunity We can potentially mitigate this ‘centrifugal’ force toward division by deploying ranking systems that do the opposite—that provide a countervailing ‘centripetal’ or bridging force. Bridging-based ranking rewards behavior that bridges divides. For example, imagine if Facebook rewarded content that led to positive interactions across diverse audiences, including around divisive topics.1 How might that change what people, posts, pages, and groups are successful? This report explores the potential of bridging and discusses some of the most common objections, addressing questions around legitimacy and practicality. It contrasts bridging with some of the most discussed approaches for reforming ranking: reverse-chronological feeds, ‘middleware’, and ‘choose your own ranking system’. (Unfortunately, without introducing bridging, all of these proposed reforms still reward those who seek to divide.) Finally, this report explores early examples where bridging systems are already being tried with some success.2 1
What ‘positive interactions’ means will depend on the product and bridging implementation. It may be explicit based on user reactions and comments or implicit based on user behavior.
2
While we do not go into the technical details here, a collaboration will be publishing on this shortly—and the example of Twitter Birdwatch includes the complete code for their limited experimental implementation.
1
Bridging-Based Ranking
