Chronoberg is a 250‑year English book corpus from Project Gutenberg, enriched with temporal annotations and affective lexicons (Valence‑Arousal‑Dominance). It allows researchers to track lexical semantic change over time and to evaluate how large language models handle sentiment and discriminatory language across different historical periods. The authors demonstrate that current LLMs struggle with diachronic shifts, underscoring the need for temporally aware training and evaluation pipelines.
By Niharika Hegde, Subarnaduti Paul, Lars-Joel Frey, Manuel Brack, Kristian Kersting, Martin Mundt, Patrick Schramowski
arXiv:2610.31874v1 Announce Type: new
Abstract: Computational framing analysis is dominated by supervised classifiers that require large annotated corpora and predefined frame taxonomies, limiting th...
By Hager Hesham Mustafa (Anmat Computational Analysis Lab)
The study analyzes millions of German-language online articles and tweets from 2019–2022 to uncover political biases using automated text analysis. It finds that international events such as the COVID‑19 pandemic and the Ukraine war create thematic convergence between German and Swiss media, while domestic policy differences drive divergence in locally focused topics. Newspapers maintain more stable political content, whereas Twitter shows rapid, event‑driven spikes, illustrating how media platforms differ in intensity and timing.
By Yara D\"oring, Felix Bie{\ss}mann
CHRONOBERG is a temporally structured corpus of English book texts covering 250 years, curated from Project Gutenberg and enriched with temporal annotations. It enables quantification of lexical semantic change via time‑sensitive Valence‑Arousal‑Dominance analysis and the creation of historically calibrated affective lexicons. Experiments show that language models trained sequentially on CHRONOBERG struggle to encode diachronic shifts, highlighting the need for temporally aware training and evaluation pipelines.
By Niharika Hegde, Subarnaduti Paul, Lars Joel-Frey, Manuel Brack, Kristian Kersting, Martin Mundt, Patrick Schramowski
arXiv:2405. 17838v3 Announce Type: replace-cross Abstract: Socio-linguistic indicators of affectively-relevant phenomena, such as emotion or sentiment, are often extracted from text to better understand features of human-computer interactions, including on social media.
By Keith Burghardt, Daniel M. T. Fessler, Chyna Tang, Anne Pisor, Kristina Lerman
Was this person ever at that place, and if so, when? Answering such questions from noisy, multilingual historical documents is the central challenge of HIPE-2026, the third edition of the HIPE evaluation series.
arXiv:2609.37498v1 Announce Type: new
Abstract: Wikipedia aims to provide a unified, neutral record of history, yet its independent language editions often function as distinct epistemic communities,...
By Claudiu Creanga, Liviu P. Dinu, Anca Dinu
arXiv:2608. 03507v1 Announce Type: cross Abstract: Historical language change affects morphology, syntax, semantics, and pragmatics, yet computational studies typically examine these levels with incompatible representations and therefore cannot determine whether they evolve together across languages.
By Gagan Bhatia, Julian Schlenker, Simone Paolo Ponzetto, Steffen Eger
arXiv:2609.08609v2 Announce Type: replace
Abstract: Tracking semantic change in low-resource languages across extensive historical timelines presents significant challenges due to data scarcity and t...
By Nevidu Jayatilleke, Nisansa de Silva
arXiv:2608. 19472v1 Announce Type: new Abstract: Lexical semantic change (LSC) is commonly modelled through vector-space representations, but these approaches often provide limited insight into which aspects of usage are changing.
By Bach Phan-Tat, Kris Heylen, Dirk Geeraerts, Stefano De Pascale, Dirk Speelman
The paper introduces Coupled Usage–Sense Processes (CUSP), a method that models lexical semantic change by coupling contextual distributions through latent usage components and using Markov composition to link adjacent time periods. CUSP quantifies change magnitude and timing, separates variation into component movement and internal reorganization, and attributes changes to specific transported component pairs. The approach is validated on synthetic data, English and German corpora, and a large corpus of US court opinions, providing detailed, text‑grounded insights into how word meanings evolve over time.
By Haruka Ezoe, Ryohei Hisano
The paper introduces an unsupervised framework that identifies and characterizes competing narratives in political discourse on social media, specifically analyzing German politicians' tweets. It uses a multi‑stage pipeline incorporating topic modeling, event detection, and event linking to form coherent stories and reveal distinct user community perspectives. Two case studies on polarizing issues demonstrate the method’s effectiveness in uncovering divergent viewpoints and framing conflicts around trending political topics.
By Sergej Wildemann, Erick Elejalde