HistLens: Mapping Idea Change across Concepts and Corpora
2026-04-13 • Computation and Language
Computation and Language
AI summaryⓘ
The authors introduce HistLens, a new computer method for studying how ideas change over time in different texts and sources. Unlike previous tools that usually focus on just one idea or text, HistLens looks at multiple ideas across many collections of writings. It breaks down ideas into understandable parts and tracks how these parts change, even when ideas aren’t directly mentioned. Their tests show HistLens can compare idea changes across different texts and help researchers in social sciences and humanities analyze historical language in a clearer way.
Semantic evolutionDiachronic semanticsConceptual historyMulti-corpus analysisSurface lexical evidenceSAE-based frameworkImplicit concept computationInterpretive granularitySocial processesPress corpora
Authors
Yi Jing, Weiyun Qiu, Yihang Peng, Zhifang Sui
Abstract
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.