The study analyzes 13.8 million tweets from parliamentarians in 26 democracies and finds that severe political incivility nearly doubled from about 2.7 % in 2017 to 5.8 % in 2022. Populist parties are the strongest predictor of incivility, especially when in opposition, while stronger liberal democracies are associated with lower levels of severe incivility. The increase is largely behavioral, with mainstream parties becoming more aggressive, leading to a narrowing gap between radical and mainstream parties’ uncivil rhetoric.
By Petter T\"ornberg, Juliana Chueri
arXiv:2609.15207v1 Announce Type: new
Abstract: Generative AI writing assistants and the Large Language Models (LLMs) that power them are increasingly part of how voters gather information before ele...
By Bastiaan Bruinsma, Annika Fred\'en, Paul R\"ottger, Moa Johansson, Asad Sayeed
The study examines how political narratives formed and spread on X, Facebook, and Reddit during the 2024 Moldovan presidential election. Using multilingual stance detection, network analysis, and causal inference, it finds distinct cross‑platform differences in positions on European integration, Russia, and the candidates, and shows that amplification and peer exposure significantly reinforce these narratives. This is the first comprehensive analysis of social media discourse for this election, highlighting the role of language, network dynamics, and causal effects in shaping political influence.
By Andrea Piras, Osman Rahmoun, Elena Zheleva
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
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
The paper examines how six foundational theories of intergroup hostility—boundary construction, threat construction, scapegoating, negative evaluation, dehumanization, and action orientation—manifest in online discourse. Using 2.86 million posts from TikTok, Truth Social, and Twitter/X during the 2024 U.S. presidential election, the authors model these mechanisms within a common empirical framework and find that boundary and threat construction anchor the system, with a regular temporal ordering of rhetorical strategies. The study maps theoretical frameworks onto real-world discourse, offering a clearer empirical basis for computational social science to model intergroup hostility beyond single-label detection.
By Patrick Gerard, Julia Mendelsohn, Kristina Lerman
arXiv:2610.03077v1 Announce Type: new
Abstract: Propaganda detection is an essential task in natural language processing (NLP), particularly in the context of manipulative political communications. H...
By Claudiu Creanga, Ioachim Lihor, Liviu P. Dinu
arXiv:2508.16013v2 Announce Type: replace
Abstract: Large language models (LLMs) are increasingly deployed in politically sensitive contexts, raising concerns about their susceptibility to ideologica...
By Pietro Bernardelle, Stefano Civelli, Leon Fr\"ohling, Riccardo Lunardi, Kevin Roitero, Gianluca Demartini
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 introduces a two‑dimensional framework to audit French news headlines, distinguishing salience framing—captured by four wording devices—from selection framing—captured by outlet‑level story form and high‑charge distributions. Using a 10,000‑headline supervision set annotated by LLMs and human arbitration, the authors classify 902,111 headlines from 25 outlets (2022‑2025) and find that salience and selection diverge yet correlate, that default thresholds inflate salience estimates, and that headlines mentioning Jews, the Far‑right, and Muslims exhibit the highest salience rates. The authors release their dataset, lexicons, and code, claiming it is the largest French headline framing audit to date.
By Amr Sobhy
arXiv:2606. 28335v1 Announce Type: cross Abstract: We argue, with systematic empirical evidence, that a large language model's political ideology is not a fixed point, but a conditional distribution $\mathbb{P}($position$\mid$context$)$ over a real political space.
By Adib Sakhawat, Syed Rifat Raiyan, Tahsin Islam, Takia Farhin, Hasan Mahmud, Md Kamrul Hasan
arXiv:2606.12186v2 Announce Type: replace
Abstract: Enthymemes, arguments with unstated premises or conclusions, are pervasive in persuasive discourse, yet their annotation remains notoriously subjec...
By Martial Pastor, Nelleke Oostdijk