Creative lives are full of encounters with admirable role models and inspirational works. How does learning from them enable creators to accomplish distinguished achievements? This question of how creative individuals develop through interaction and context is vital to the development of creative expertise. However, quantitatively addressing this question has been an unsolved issue. In this paper, to answer this question, we use a methodological framework to analyze quantitatively individual-context dynamism that leads to creative development. Using this methodology, we investigate how acquired domain knowledge affects changes in creative expression across a lifespan. Poetic compositions by the French poet, Stéphane Mallarmé, are analyzed. Machine learning and natural language process methods, rolling stylometry in the authorship attribution realm, and Structural Topic Modeling, are applied to examine longitudinal changes in the styles and content of the poetry. The results suggest that inspiration by other creators’ works influenced Mallarmé’s creative activities over time, and eventually, he digested such experiences and developed his own style of expression. Our study provides a specific case of how role models and mentors enhance creative development using modern authorship attribution methods.
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