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
arXiv:2607. 14197v1 Announce Type: new Abstract: Artificial Intelligence (AI) answer engines now field a growing share of the questions that analysts, scholars, and the public ask about issues of peace and conflict.
By Jason Miklian
arXiv:2512. 15792v3 Announce Type: replace-cross Abstract: Large language models (LLMs) have rapidly become indispensable tools for acquiring information and supporting human decision-making.
By Xulang Zhang, Rui Mao, Erik Cambria
arXiv:2608. 06123v1 Announce Type: new Abstract: Measuring political bias in large language models (LLMs) remains challenging as it can manifest through subtle differences in framing, argumentation, and legal reasoning that are difficult to capture with a single metric.
By Massi-Nissa Abboud, Aladin Djuhera, Elena Cabrio, Holger Boche
arXiv:2512. 15792v4 Announce Type: replace-cross Abstract: Large language models (LLMs) have rapidly become indispensable tools for acquiring information and supporting human decision-making.
By Xulang Zhang, Rui Mao, Erik Cambria
arXiv:2509.22367v3 Announce Type: replace
Abstract: Large language models (LLMs) reflect politically-slanted opinions in their generated text. Even though it is widely assumed that model behavior ste...
By Tanise Ceron, Dmitry Nikolaev, Dominik Stammbach, Debora Nozza