Digital archives and AI in reconstructing transnational historical networks

Thesis Abstract

Digital archives and AI enable the reconstruction of transnational historical networks by integrating heterogeneous archival sources, scalable data curation, and machine-assisted inference to illuminate cross-border cultural, economic, and political linkages that have been fragmented by digital silos and historiographical silences. The study addresses the problem that traditional archival methods and narrative histories often overlook informal and secondary networks spanning continents, due to disparate cataloging practices, language barriers, and incomplete provenance data. The aim is to develop a replicable, AI-assisted framework for detecting, mapping, and analyzing transnational historical networks through digital archives, thereby enhancing the accuracy and granularity of cross-border historical understandings. Specific objectives include (1) constructing a multilingual digital archive corpus from primary sources, secondary accounts, and archival metadata across Europe, Asia, Africa, and the Americas; (2) designing an AI-enabled workflow for named-entity recognition, temporal linkage, and relationship extraction to identify actors, flows, and institutions; (3) validating network reconstructions against established historiographies using triangulated qualitative and quantitative measures; (4) assessing how network structures shift over key historical periods, such as the late 19th and mid-20th centuries; and (5) evaluating the methodological implications of AI-assisted history for epistemic access, reproducibility, and bias mitigation. The methodology adopts a mixed-methods, exploratory research design underpinned by actor-network theory (ANT) and social network analysis (SNA). The population comprises digitized archival holdings, newspapers, correspondence, and organizational records drawn from 12 national and regional archives, libraries, and digital repositories, spanning 1870–1960. A purposive sample of 2000 cataloged items with multilingual metadata and 150 curated collections will be compiled, supplemented by 60 expert interviews with historians specializing in transnational studies. Data collection employs a multimodal toolkit (i) automated web-scraping and OCR-processed digitized texts, (ii) translation pipelines using neural machine translation to normalize multilingual content, (iii) an NLP suite applying named-entity recognition, relation extraction, and event extraction, and (iv) metadata harvesting to build equivalent nodes and ties. Instrument validity will be enhanced through pilot testing on a subset of 300 items and cross-checking with domain experts; reliability will be assessed via inter-annotator agreement for a manual subset (? ? 0.70). The analytical framework integrates quantitative and qualitative methods. Network construction will be operationalized with node-edge matrices and dynamic network models, employing social network analysis metrics (degree centrality, betweenness, eigenvector centrality, community detection) to identify influential actors and clusters. Temporal network analysis will be conducted using time-sliced networks and event-based modeling to capture shifts in relations. The study will apply regression analyses to test hypotheses about the relationship between network prominence and archival provenance factors (collection size, accessibility, language diversity), and thematic analyses of extracted narratives to elucidate the nature of cross-border interactions. A validation phase will compare AI-generated networks with established historiographical syntheses and randomized sensitivity analyses will assess robustness to data sparsity and language biases. Key expected findings include (1) a high-density core of transnational actors (e.g., international NGOs, trade coalitions, diasporic networks) that mediated information and material flows across imperial and postcolonial spaces; (2) demonstrable undercurrents of connectivity between urban and port cities revealing shared infrastructures, epistemic communities, and fundraising channels; (3) temporal patterns aligning with major geopolitical shifts, such as decolonization and globalization waves, evidenced by network restructuring; and (4) methodological insights into the strengths and limitations of AI-driven archival reconstruction, including the handling of ambiguous provenance and multilingual semantic variance. The study contributes to knowledge by proposing a transparent, scalable AI-driven methodology for reconstituting transnational historical networks, offering a replicable workflow for researchers in History and International Studies, and advancing methodological debates on digital historiography, data provenance, and bias mitigation. The primary conclusion anticipates that AI-assisted integration of digital archives substantially enhances the detection of cross-border linkages and yields richer, more nuanced networked histories. Recommendations include expanding multilingual archival collaboration, developing open-access kernels for network reconstruction, and fostering interdisciplinary training in digital methods for historians to ensure methodological rigor and interpretive accountability.