This paper explores how Digital Humanities (DH) methods can be applied to the analysis of literary style in translation, with a focus on narrative point of view. Framed within DH debates on the relationship between computational analysis, interpretive practices, and analytical scale, it argues for an integrated approach in which close and distant reading function as complementary methodologies. By combining corpus stylistics and translation studies within a DH framework, the study demonstrates how digitally assisted methods can enhance interpretive analysis in the humanities. The case study examines Virginia Woolf’s "Mrs Dalloway" (1925) and its most recent Italian translation, "La signora Dalloway" (2024). In Woolf’s modernist prose, point of view is a central stylistic category, realised through techniques such as Free Indirect Discourse, deictic shifts, parenthetical structures, and the blending of narrative and character voices. These features pose particular challenges for DH approaches, as they rely on subtle, distributed linguistic cues rather than easily isolable traits. Methodologically, the study adopts an interlingual parallel corpus model, aligning the source and target texts in Sketch Engine to investigate how linguistic markers of point of view – including deictic adverbs, demonstratives, reporting verbs, verba sentiendi, and the connectives but and for – are preserved or modified in translation. Quantitative frequency analysis is combined with close reading of parallel concordances to trace cumulative stylistic effects. To foreground DH tool diversity, the analysis is complemented by a comparison with Voyant Tools, whose visualisation oriented environment highlights differences in analytical scale, affordances, and interpretive workflow. The findings show that while many stylistic patterns are retained, cumulative micro level shifts – particularly in the translation of deixis – produce a gradual harmonisation of narrative perspective. Overall, the paper positions literary translation as a privileged site for DH research into style, variation, and interpretive choice.

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 study, a GPT-2-style transformer model (223M parameters) is pre-trained on a large, high-quality corpus of modern Chinese text (FineWeb Edu Chinese V2.1) to establish a neutral linguistic baseline. The model is then fine-tuned for five epochs exclusively on the Selected Works of Mao Zedong 毛澤東選集 to simulate a reader becoming saturated with “Maospeak,” the highly formulaic and militant language of the Mao era (1949-1976). The core metric is perplexity, which quantifies the model’s predictive uncertainty, or “surprise,” at each token. By tracking the largest decreases in perplexity, the study identifies the core phraseology of Maoism, including canonical lists of class enemies and rigid rhetorical patterns. This “cognitive overfitting” illustrates how propaganda automates language to create a predictable, low-perplexity environment and legitimize violence. N-gram entropy analysis confirms that the Mao corpus is measurably more repetitive than a literary corpus at smaller n-gram sizes, that is, in its foundational vocabulary and short collocations. Concurrently, as the model specializes in Maospeak, its average perplexity on a representative corpus of 100 modern Chinese novels increases. Microscopic analysis of novels by Zhang Wei 張煒, Lilian Lee 李碧華, and Dung Kai-cheung 董啟章 reveals how literary style creates “non-anomalous surprise” through high-perplexity moments, such as creative metaphors, the use of classical Chinese syntax, and the abrupt insertion of non-standard Chinese languages such as Cantonese. By visualizing token sequences as perplexity landscapes with peaks and valleys, the study reconceives style as a probabilistic phenomenon and showcases the potential of “cognitive stylometry” for literary theory and close reading.

This paper presents a computational approach to redaction criticism in the Book of Isaiah, utilising a dual-model Transformer ensemble to detect stylistic discontinuities at the verse level. Rather than classifying text into predefined authorial strata, the method identifies locations where linguistic expectations shift abruptly. A causal language model (GPT-Neo) measures contextual disruption via perplexity, whilst a masked language model (DictaBERT) evaluates internal morphosyntactic coherence via pseudo-log-likelihood. These signals are normalised and synthesised into a Combined Fracture Index (CFI) across Isaiah’s 1,291 verses (Westminster Leningrad Codex).

The empirical results align with established biblical scholarship. The macro-structural transition between chapters 39 and 40 exhibits a severe statistical shift, with Isa 40:1 registering one of the strongest spikes in the corpus. The Deutero/Trito-Isaiah boundary is similarly detected, marked by a steep CFI drop from 55:13 to 56:1. Furthermore, verses within the Fourth Servant Song (notably 53:8 and 53:11) rank among the most anomalous overall, supporting its status as a distinct compositional unit.

Crucially, the method isolates unexpected micro-fractures. Chapter 28 emerges as the most internally spliced chapter, containing two of the highest global CFI scores (28:12, 28:17). Chapters 32-33 form a statistically unstable 'hot bridge'. In Deutero-Isaiah, dramatic spikes at 40:19 and 46:5 provide independent quantitative evidence that these anti-idol polemics are stylistically alien interpolations. Finally, Isa 63:13 appears as a severe, unexpected outlier within Trito-Isaiah.

Having validated that this methodology reliably detects physical editorial seams in Isaiah, the fully implemented computational pipeline is ready for deployment across the wider Hebrew Bible. This scalable infrastructure offers a transparent, replicable framework for generating new quantitative data to complement traditional philological inquiry