Feedback Shaping: A Modeling Approach to Nurture Content Creation
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 7 of its 40 references that are in the map. First posted on arXiv 21 Jun 2021.
Builds on
7 of 40 references.
References · 7
- 2016XGBoost: A Scalable Tree Boosting Systemcited by 5,120
- 2016Wide & Deep Learning for Recommender Systemscited by 745
- 2016Identification and estimation of treatment and interference effects in observational studies on networkscited by 43
- 2013Graph cluster randomization: network exposure to multiple universescited by 42
- 2013Estimating Average Causal Effects Under General Interference, with Application to a Social Network Experimentcited by 10
- 2019Using Ego-Clusters to Measure Network Effects at LinkedIncited by 3
- 2017Randomized experiments to detect and estimate social influence in networkscited by 1
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Citing papers per year: 2026: 1.
1 paper.