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: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: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
arXiv:2608.29198v1 Announce Type: new
Abstract: As Large Language Models (LLMs) increasingly encourage users to disclose personal profiles for tailored assistance, measuring their political alignment...
By Li-Ni Fu, Chang-Chih Meng, Chien-Hua Chen, Hen-Hsen Huang, I-Chen Wu
The study evaluates large language models (LLMs) that generate political networks by comparing them to survey-reported partisan exposure from the ANES 2020–2022 Social Media Study. It finds that while network structure and political assortativity vary across model configurations, many models either over‑separate Democratic and Republican egos or underestimate Republican exposure, leading to group‑specific errors in local partisan environments. These discrepancies persist even among networks with similar aggregate properties, indicating that aggregate metrics alone can mask important individual‑level inaccuracies.
By Marco Tonin, Michele Tizzoni, Bruno Lepri, Luca Maria Aiello
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
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:2610.16498v1 Announce Type: cross
Abstract: Generative Social Simulation (GSS) leverages Large Language Models (LLMs) to create sophisticated personas that define agents with demographics, emot...
By Dimitris Michailidis, Petter T\"ornberg, Fernando P. Santos
The study analyzes blame attribution in the Danish Parliament from 1997 to 2026 using a custom classifier, BlameBERT, and multilevel modeling. Results show a banana-shaped trend: blame fell until about 2016, then rose sharply from 2019 onward. Blame is consistently higher by opposition parties, especially among right-wing and ideologically extreme groups, indicating an ideologically asymmetric hardening of political discourse.
By Markus Lundsfryd Jensen, Rune Egeskov Trust, Kenneth Christian Enevoldsen, Sara Kolding
arXiv:2607. 29334v1 Announce Type: cross Abstract: Conversational AI developed by geopolitical rivals reaches citizens worldwide, raising concerns that it could sway public opinion or be rejected as foreign propaganda, with consequences for democratic discourse and information sovereignty.
By Ningzhi Liu, Yannic Hinrichs, Jonas R. Kunst
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: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