Social networks rely on sharing engaging content for their users. Since continued production of user-generated content is critical to their success, they have constructed a variety of tools to motivate new content creation, to facilitate user discovery of new content, and to provide attention and recognition to the best user-generated content. Past research shows that such attention and recognition increases the volume of content shared on the networks.
But how do these affect the nature of content shared on their platforms? Do they cause creators to share content similar to the ones that received attention and recognition? Or do creators take risks and create different content than the ones recognized? These are the questions we ask in this paper.
Our empirical context is an image-sharing social network, where creators share digital art and photography. We leverage a randomized controlled experiment to induce exogenous variation in attention and recognition to specific content. Using a transfer learning-based machine learning algorithm we convert complex images into lower-level features. This allows us to analyze similarities and differences between images.
Our main findings are that creators produce and share different content on the social network, than the ones that received attention and recognition. This result is robust to a variety of ways in which we classify image content. Our results illustrate the importance of tools aimed to induce attention and recognition to the creation and development of diverse content by social media creators, and give insights into factors that motivate content creators to create content.