AI Can Enhance Creativity in Social Networks
This addresses the problem of boosting creativity for users in social networks, but it is incremental as it builds on existing recommendation and modeling techniques.
The study tackled the problem of enhancing creativity in social networks by developing SocialMuse, a peer recommendation system that uses a model to predict and maximize ideation performances, resulting in treatment networks outperforming control networks in creativity measures and becoming more decentralized.
Can peer recommendation engines elevate people's creative performances in self-organizing social networks? Answering this question requires resolving challenges in data collection (e.g., tracing inspiration links and psycho-social attributes of nodes) and intervention design (e.g., balancing idea stimulation and redundancy in evolving information environments). We trained a model that predicts people's ideation performances using semantic and network-structural features in an online platform. Using this model, we built SocialMuse, which maximizes people's predicted performances to generate peer recommendations for them. We found treatment networks leveraging SocialMuse outperforming AI-agnostic control networks in several creativity measures. The treatment networks were more decentralized than the control, as SocialMuse increasingly emphasized network-structural features at large network sizes. This decentralization spreads people's inspiration sources, helping inspired ideas stand out better. Our study provides actionable insights into building intelligent systems for elevating creativity.