Relationships between characters are not just themes in a story but key elements that shape how plots unfold. This article presents a large-scale study of relational arcs, the trajectories of ties, such as kinship, romance, alliance and enmity as they rise and fall across the course of a novel. We build on the Artificial Relationships in Fiction dataset, which contains over 120,000 automatically annotated relationships from 96 novels published between 1850 and 1950. Our study makes four contributions. First, we show that relationship dynamics can be modeled as arcs that highlight recurring narrative patterns, such as conflicts peaking near the climax or romances resolving toward the end. Second, we use temporal normalization to compare books of very different lengths, allowing us to identify consistent trends across the corpus. Third, we demonstrate that genres and historical periods leave clear relational “fingerprints.” For instance, domestic fiction emphasizes family ties, while adventure stories highlight shifting alliances and adversaries. Finally, we cluster arcs into four common shapes (Rise, U-shape, Decline and Oscillating) that echo well-known narrative prototypes. By bringing narratology together with modern natural language processing, we argue that relationships provide a measurable grammar of plot. This approach offers new resources for literary analysis, new methods for computational modeling of narrative, and fresh evidence about how cultural storytelling patterns change over time.

Autoregressive language models generate text by predicting the next word from the preceding context. The regularities internalized from specific training data make this mechanism a useful proxy for historically situated readerly expectations, reflecting what earlier linguistic communities would find probable or meaningful. In this article, I pre-train a GPT model (223M parameters) on a broad corpus of Chinese texts (FineWeb Edu Chinese V2.1) and fine-tune it on the collected writings of Mao Zedong (1893–1976) to simulate the evolving linguistic landscape of post-1949 China. Identifying token sequences with the sharpest drops in perplexity – a measure of the model’s surprise – reveals the core phraseology of “Maospeak,” the militant language style that developed from Mao’s writings and pronouncements. A comparative analysis of modern Chinese fiction demonstrates how literature becomes unfamiliar to the fine-tuned model, generating perplexity spikes of increasing magnitude. The findings suggest a mechanism of attentional control: whereas propaganda backgrounds meaning through repetition (cognitive overfitting), literature foregrounds it through deviation (non-anomalous surprise). By visualizing token sequences as perplexity landscapes with peaks and valleys, the article reconceives style as a probabilistic phenomenon and showcases the potential of “cognitive stylometry” for literary theory and close reading .