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
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
arXiv:2607. 21842v1 Announce Type: cross Abstract: Research on political polarization on social media depends on the ability to reliably measure partisanship in user-generated content.
By Fathima Ameen, Christopher G. Healey
The paper introduces TSN4PI, a unified framework for tracking the evolution of political ideologies on social media. It combines a PIDN module that uses large language models, style transfer, and unsupervised domain adaptation to detect ideologies and filter noise, with a PIPN module that employs temporal graph neural networks to predict future ideological shifts. The authors release two large-scale datasets and validate the approach on platforms such as X and Truth Social, offering empirical insights into political polarization and online ideology evolution.
By Yijie Xu, Chao Wang, Hui Xiong
The paper introduces TSN4PI, a unified framework for tracking the evolution of political ideologies on social media. It combines a PIDN module that uses large language models, style transfer, and unsupervised domain adaptation to detect ideologies and filter noise, with a PIPN module that employs temporal graph neural networks to predict future ideological shifts. The authors release two large-scale datasets and validate the approach on platforms such as X and Truth Social, offering empirical insights into political polarization and ideology evolution.
The study introduces a computational method to distinguish between antagonizing and criticizing rhetoric in political discourse on Twitter/X. Analyzing over 17 million tweets from German trending topics, it finds that roughly one‑third contain antagonizing rhetoric, which is more prevalent in trending discussions than in general Twitter traffic. Right‑leaning users use antagonizing language more than left‑leaning users, and both forms of rhetoric are amplified within political camps, with certain parties—AfD and the Greens—emerging as primary antagonists.
By Armin Pournaki, Eckehard Olbrich
arXiv:2609.38256v1 Announce Type: new
Abstract: Large language models (LLMs) are increasingly used to answer questions about politically contentious issues, yet evaluations typically treat a model's...
By Olivia Macmillan-Scott, Michael Jacobs, Nils Metternich, Mirco Musolesi
The study examines how language influences AI chatbot responses to questions about the war in Ukraine, revealing that the same AI systems (GPT, Claude, Gemini) produce varying political stances across 112 languages. By evaluating 20 statements in 112 languages, the researchers found that Russia‑leaning versus Ukraine‑leaning answers differ by language, mirroring global political attitudes such as public support for Russia, UN voting patterns, and aid levels. This pattern persists across all three models and even when specific statement pairs are removed, suggesting that information warfare could embed geopolitical biases into AI training data.
By Maxim Chupilkin
arXiv:2608. 05155v1 Announce Type: cross Abstract: Traditional sentiment analysis (SA) models, while effective for polarity classification, provide limited insight into the rhetorical, ideological, and framing dimensions of political discourse -- dimensions that are central to research in the social sciences and humanities (SSH).
By Maryam Fooladi, Federico Bottino
arXiv:2610.05275v2 Announce Type: replace
Abstract: A large part of political discourse on social media is built and expressed at a level of claims: i.e. declarative, typically single-clause statemen...
By Sandrine Chausson, Bj\"orn Ross
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:2608.21385v1 Announce Type: cross
Abstract: Social media has become a central arena in which armed conflicts are contested, yet the pro-Israel and pro-Palestine communities on Telegram, whose b...
By Michail Zafeiropoulos, Despoina Antonakaki, Sotiris Ioannidis