Quantitative Intertextuality from the Digital Humanities Perspective: A Survey

TL;DR

This survey reviews recent advances in quantitative intertextuality research in digital humanities, highlighting methods from statistics to deep learning, and discusses their applications across languages and disciplines.

Contribution

It provides a comprehensive roadmap of data, methods, and applications in quantitative intertextuality, integrating recent technological advances and interdisciplinary potential.

Findings

Large-scale intertextuality studies are increasing in diversity and precision.

Deep learning methods are becoming prominent in intertextuality analysis.

Intertextuality research is expanding across multiple languages and disciplines.

Abstract

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.