Seshat: Decentralizing Oral History Text Analysis

Lin Wang, Lehao Lin, Xiao Wu, Rongman Hong

Abstract: Text analysis tools are often utilized to store and analyze massive texts in modern oral history research. However, the state-of-the-art centralized text analysis systems are suffering from data synchronization, maintenance, and cross-platform compatibility issues in a stand-alone environment, while the server-based ones are struggling from the lack of commitment to long-term support and unforeseen security risks, e.g. data leakage and loss. In this work, Seshat, a decentralized oral history text analysis system, employs Inter-Planetary File System (IPFS) storage, blockchain, and web technologies to address these issues. With Seshat, the text processing operations are localized in users’ terminals, while the data and analytical logics are permanently preserved on the blockchain. Experiments are conducted to validate the performance of the proposed system. By sacrificing affordable text processing time, Seshat shows better robustness and compatibility to facilitate effective digital assistance for text analysis applications like oral history studies.