HistLens: Mapping Idea Change across Concepts and Corpora
Provides social scientists and humanities researchers with a more granular and comparable method for diachronic text analysis across concepts and sources.
HistLens introduces a unified SAE-based framework for analyzing semantic change of multiple concepts across multiple corpora, enabling comparable tracking of conceptual trajectories. Experiments on press corpora demonstrate cross-concept and cross-corpus computation of idea evolution patterns.
Language change both reflects and shapes social processes, and the semantic evolution of foundational concepts provides a measurable trace of historical and social transformation. Despite recent advances in diachronic semantics and discourse analysis, existing computational approaches often (i) concentrate on a single concept or a single corpus, making findings difficult to compare across heterogeneous sources, and (ii) remain confined to surface lexical evidence, offering insufficient computational and interpretive granularity when concepts are expressed implicitly. We propose HistLens, a unified, SAE-based framework for multi-concept, multi-corpus conceptual-history analysis. The framework decomposes concept representations into interpretable features and tracks their activation dynamics over time and across sources, yielding comparable conceptual trajectories within a shared coordinate system. Experiments on long-span press corpora show that HistLens supports cross-concept, cross-corpus computation of patterns of idea evolution and enables implicit concept computation. By bridging conceptual modeling with interpretive needs, HistLens broadens the analytical perspectives and methodological repertoire available to social science and the humanities for diachronic text analysis.