CrowdChess

Recently, gaming live-streams appeared in which the audience interacts without a streamer. These settings sometimes allow the audience to select from a set of input aggregators to mediate how individual contributions are aggregated. We developed CrowdChess, a system which is optimized for the live-streaming context: multiple viewers play chess together in a single game instance against an AI; they suggest moves and further select one of six aggregators to mediate their contributions. In contrast to existing approaches, CrowdChess allows reasoning about the quality of individual and aggregator contributions, and thus it serves as a test bed for how effectively an audience interacts in such settings. We conducted a study in a live-streaming setup (n=12) and contribute insights on the player perception of audience-driven games and show how aggregators in such a small-scale setting are used, for example, the tendency to use group-based aggregators instead of empowering an individual.