H-gear

This tool analyzes the transmission of political ideas during the American Revolution. It enables users to track shifts in concepts in letter correspondence networks, using semi-supervised topic modeling and temporal social network modeling to reveal ideological shifts and identify key influencers.

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What H-gear can do for you

H-GEAR (Acronym for Historiographing the Era of the American Revolution) is an advanced tool for analyzing the transmission of political ideas during the American Revolution, seamlessly integrated with ShiCo (Shifting Concepts Through Time) software. This integration allows users to track shifts in key political concepts, such as liberty and freedom, by examining the evolution of language over time. thinking evolved over time.

H-GEAR leverages ShiCo to analyze the changing vocabulary around political ideas. By providing a set of keywords, users can explore how terms like “freedom” and “democracy” evolved, offering deep insights into the ideological landscape of the Revolutionary era.

A key focus of the developed tool is on the distinction between liberal and republican ideas and how they were transmitted differently through correspondence networks. It combines semi-supervised topic modeling to identify and select politically-motivated letters with path-based temporal social networks to trace how political discourse spread between influential figures, accounting for the timing and flow of communication.

By focusing on correspondence data such as Founders Online and the Letters of Delegates to Congress, H-GEAR enables to map out networks of communication, pinpointing how and where political ideas were propagated. The network analysis helps identify the key actors who shaped these ideological shifts and tracks the spread of these ideas over time.

The software provides a robust framework for researchers to examine the interplay between language and social networks, offering a nuanced understanding of how political concepts were transmitted and transformed during the American Revolution. More generally, it allows for a systematic analysis of idea propagation in a digitized corpus of heterogenous letter data, revealing the central figures and networks driving change in thinking about a particular concept.

Participating organisations

Netherlands eScience Center
University of Groningen

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