Balancing novelty and familiarity: Nonlinear effects of semantic progression on audience engagement

Abstract

Research on information diffusion suggests that audience engagement often depends on a balance between novelty and familiarity, but it remains unclear whether narratives follow a similar principle when organizing information for audiences. To examine this possibility, this study analyses 2578 TED talks and models narrative semantic progression along three dimensions: speed, breadth, and circuitousness. The results reveal that semantic breadth and circuitousness are associated with viewership in a nonlinear manner, with moderate levels linked to higher engagement, while both low and high extremes are less effective. In contrast, semantic speed is predominantly negatively associated with viewership. The interaction between semantic speed and semantic breadth further indicates that breadth influences the level of speed at which predicted viewership reaches its maximum. These findings indicate that narratives associated with higher engagement tend to be characterized not by maximum novelty or familiarity, but by a balance between the two. Analyses of podcast data reveal broadly consistent patterns, extending the findings beyond the highly curated context of TED Talks. This study advances a nonlinear perspective on narrative effectiveness and demonstrates the value of Transformer-based embeddings, including large language model embeddings and widely used sentence embedding models, for capturing how meaning unfolds across narrative sequences.