Information diffusion in social networks is uneven: some content spreads much more than other content, shaping what people see. The mix of what gets shared can leave users with a misleading sense of how often things happen. Prior research primarily examines content attributes and user attributes but has largely overlooked the role of exposure frequency—how often a user encounters an event category relative to others in their information stream. We argue that exposure frequency is a key factor influencing sharing behavior. Drawing on perceptual bias and variety-seeking, we theorize that users are more likely to share low-exposure frequency (rare) event categories. As these individual decisions accumulate, rare categories become disproportionately represented—a systematic distortion that we call rareness-biased diffusion (RBD). Across six experiments and a network simulation, we show that individuals disproportionately share rare events. At the individual level, the tendency to share rare events weakens when perceptual bias or variety seeking is suppressed but strengthens when sharing opportunities increase. Temporal clustering of rare events further reduces sharing by making rare events seem common. At the network level, distortion amplifies with distance from the source and is most stable in chain networks, while outcomes in small-world and preferential-attachment networks show greater variability due to overlapping exposure. Together, these findings introduce category-level exposure frequency as a distinct predictor of sharing, establish RBD as a new diffusion construct, and highlight implications for platform design, where simple aggregation can amplify rare events and distort public understanding.
Jang et al. (2026) studied this question.