Quantitative Intertextuality from the Digital Humanities Perspective: A Survey
It provides a roadmap for researchers in digital humanities and AI, but it is incremental as it surveys existing work rather than introducing new methods.
This survey paper tackles the problem of quantitatively analyzing connections between texts (intertextuality) by reviewing data, methods, and applications from digital humanities studies, summarizing advancements from statistics to deep learning across multiple languages and topics.
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.