Brain-Based Prediction of Information Sharing

Information sharing, either online or face-to-face, shapes what information is available and impactful in modern societies. Understanding why people share can help us to better predict when sharing occurs and what the impact of a particular message might be in the population. In prior work, we have shown that neural responses to newspaper articles in a few study participants can be used to predict large-scale sharing behavior in the population of online newspaper readers. We have further theorized, based on these data, that sharing is a value-based behavior and the value of sharing increases to the extent that would-be sharers perceive opportunities to use the to-be-shared content to present themselves in a positive light or connect positively to others. In this project, we conducted a conceptual replication of this work, demonstrating the validity, reliability, and generalizability of our neural prediction model. In addition, we used the theory of value-based sharing to build novel interventions aiming to increase the likelihood that would-be-sharers identify opportunities to fulfill their sharing motives. We test causal effects of these interventions on sharing likelihood in several neuroimaging and behavioral experiments.

Research team:

Status: Data collection completed; publications are being prepared

Funding: DARPA