Quantitative Intertextuality from the Digital Humanities Perspective: A Survey
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Abstract (arXiv)
The connection between texts is referred to as intertextuality in literary theory, which served as an important theoretical basis in many digital humanities studies. Over the past decade, advancements in natural language processing have ushered intertextuality studies into the quantitative age. Large-scale intertextuality research based on cutting-edge methods has continuously emerged. This paper provides a roadmap for quantitative intertextuality studies, summarizing their data, methods, and applications. Drawing on data from multiple languages and topics, this survey reviews methods from statistics to deep learning. It also summarizes their applications in humanities and social sciences research and the associated platform tools. Driven by advances in computer technology, more precise, diverse, and large-scale intertext studies can be anticipated. Intertextuality holds promise for broader application in interdisciplinary research bridging AI and the humanities.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 9 of its 70 references that are in the map. First posted on arXiv 30 Oct 2025.
Builds on
9 of 70 references.
References · 9
- 2017Billion-scale similarity search with GPUscited by 2,497
- 2023Machine-assisted quantitizing designs: augmenting humanities and social sciences with artificial intelligencecited by 15
- 2019On the Feasibility of Automated Detection of Allusive Text Reusecited by 8
- 2023Introducing Rhetorical Parallelism Detection: A New Task with Datasets, Metrics, and Baselinescited by 7
- 2016Identification of Parallel Passages Across a Large Hebrew/Aramaic Corpuscited by 6
- 2014Patterns of Text Reuse in a Scientific Corpuscited by 5
- 2024Investigating Expert-in-the-Loop LLM Discourse Patterns for Ancient Intertextual Analysiscited by 4
- 2014Automatic Detection of Reuses and Citations in Literary Textscited by 3
- 2017The Cultural Evolution of National Constitutionscited by 2
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Citing papers per year: 2026: 2.
2 papers.