The Value of Sharing: A Neural Model of Self-Related and Social Processing in Value-Based Virality

• In Event: Audience Engagement With News (TOP FACULTY PAPER AWARDEE)

Abstract

How do ideas go viral and affect the wider population? We propose a value-based virality model suggesting (1) that two inputs—a message’s self-relevance and its perceived social impact—inform a domain-general value signal, and (2) that this message value directly affects virality outcomes. Using fMRI (N = 41), we provide evidence for this model by showing (1) significant relationships between neural activity in self-related (B = .950, SE = 0.069, p < .001) and social processing (B = .925, SE = .092, p < .001) regions during initial exposure to 80 New York Times (NYT) articles, and article-induced value-related activity, and (2) significant relationships between article-induced value-related activity in our study participants and the number of article views (B = 3.052, SE = 1.147, p < .01) and retransmissions (B = 3.806, SE = 1.117, p < .01) in the population of NYT readers according to the NYT API.

Authors

• Christin Scholz, University of Pennsylvania

• Elisa Baek, University of Pennsylvania

• Matthew Brook O'Donnell, U of Pennsylvania

• Hyun Suk Kim, University of Pennsylvania

• Joseph N. Cappella, U of Pennsylvania

• Emily Falk, University of Pennsylvania