Abstract
Text similarity analysis entails studying identical and closely similar text passages across large corpora, with a particular focus on intentional and unintentional borrowing patterns. At a larger scale, detecting repeated passages takes on added importance, as the same text can convey different meanings in different contexts. This approach offers numerous benefits, enhancing intellectual and literary scholarship by simplifying the identification of textual overlaps. Consequently, scholars can focus on the theoretical aspects of reception with an expanded corpus of evidence at their disposal. This article adds to the expanding field of historical text reuse, applying it to intellectual history and showcasing its utility in examining reception, influence, popularity, authorship attribution, and the development of tools for critical editions. Focused on the works and various editions of Bernard Mandeville (1670–1733), the research applies comparative text similarity analysis to explore his borrowing habits and the reception of his works. Systematically examining text reuses across several editions of Mandeville’s works, it provides insights into the evolution of his output and influences over time. The article adopts a forward-looking perspective in historical research, advocating for the integration of archival and statistical evidence. This is illustrated through a detailed examination of the attribution of Publick Stews to Mandeville. Analysing cumulative negative evidence of borrowing patterns suggests that Mandeville might not have been the author of the piece. However, the article aims not to conclude the debate but rather to open it up, underscoring the importance of taking such evidence into consideration. Additionally, it encourages scholars to incorporate text reuse evidence when exploring other cases in early modern scholarship. This highlights the adaptability and scalability of text similarity analysis as a valuable tool for advancing literary studies and intellectual history.
Ananth Mahadevan
Machine Learning PhD Student
My research interests include systems for Machine Learning and network science.