The diffusion of ideas and language represents a typical example of collective dynamics in complex systems, conventionally described by S-shaped models such as the logistic function and its generalizations. However, the role of subexponential growth—a slower-than-exponential pattern known in epidemiology—has been largely overlooked in social diffusion processes. Here, we present a piecewise power-law model to characterize complex growth curves with a few parameters. We systematically analyzed a large-scale dataset of approximately one billion Japanese blog articles linked to Wikipedia vocabulary, and observed consistent patterns in web search trend data (English, Spanish, and Japanese). Our analysis of 2963 items, selected for reliable estimation (e.g., sufficient duration/peak, monotonic growth), reveals that 1625 (55%) diffusion patterns without abrupt level shifts were adequately described by one or two segments. For single-segment curves, we found that (i) the mode of the shape parameter $\alpha$ was near 0.5, indicating prevalent subexponential growth; (ii) the peak diffusion scale is primarily determined by the growth rate $R$, with minor contributions from $\alpha$ or the duration $T$; and (iii) $\alpha$ showed a tendency to vary with the nature of the topic, being smaller for niche/local topics and larger for widely shared ones. Furthermore, a microbehavioral model of outward (stranger) versus inward (community) contact suggests that $\alpha$ can be interpreted as an index of the preference for outward-oriented communication. These findings suggest that subexponential growth is a common pattern of social diffusion, and our model provides a practical framework for consistently describing, comparing, and interpreting complex and diverse growth curves.

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• Complex systems
• Degree distributions
• Growth processes
• Population dynamics
• Scaling laws of complex systems
• Spreading
• Language models
• Online social networks
• Social dynamics
• Data analysis
• Mean field theory
• Spreading models
• Time series analysis
• Vertex copying models