Feedback Shaping: A Modeling Approach to Nurture Content Creation

TL;DR

This paper introduces a modeling approach to optimize social media newsfeeds by balancing feedback to incentivize content creators, thereby fostering a more active and sustainable content ecosystem.

Contribution

It presents a novel model predicting creator incentives from consumer feedback and demonstrates its deployment to enhance content creation on LinkedIn.

Findings

Significant increase in content creation activity

Maintained high-quality user experience

Effective feedback reshaping in live environment

Abstract

Social media platforms bring together content creators and content consumers through recommender systems like newsfeed. The focus of such recommender systems has thus far been primarily on modeling the content consumer preferences and optimizing for their experience. However, it is equally critical to nurture content creation by prioritizing the creators' interests, as quality content forms the seed for sustainable engagement and conversations, bringing in new consumers while retaining existing ones. In this work, we propose a modeling approach to predict how feedback from content consumers incentivizes creators. We then leverage this model to optimize the newsfeed experience for content creators by reshaping the feedback distribution, leading to a more active content ecosystem. Practically, we discuss how we balance the user experience for both consumers and creators, and how we carry out online A/B tests with strong network effects. We present a deployed use case on the LinkedIn newsfeed, where we used this approach to improve content creation significantly without compromising the consumers' experience.