Social networks utilize award recognition and front pages to motivate user content creation, facilitate consumer discovery of content, and provide attention and recognition to the best content. Past research shows that such attention and recognition increase the volume of content shared on the networks. But how do these affect the nature of content shared on platforms? Do they cause creators to share content similar to that which received attention and recognition? Or do creators take risks and create different content? We investigate these questions in the context of a digital art-sharing social network. We implement a randomized controlled experiment to induce exogenous variation in attention and recognition provided to users’ content. Using a transfer learning machine learning algorithm, we convert complex images into lower-level features to analyze changes in content novelty. We find that awarded creators produce more novel content, relative to both the awarded content and their past work. This result is robust to a variety of ways in which we classify image content. Our results illustrate the importance of tools that induce attention and recognition to the creation and development of diverse content by social media creators and give insights into factors that motivate content creation.