← Back to all news
arXiv Computation and Language By Akram Elbouanani, Yousra Taleb, Gianluca Demartini, Adrian Popescu

User Attributes and Political Ideology Shape LLM Receptiveness in Online Political Debates

Read the original on arXiv Computation and Language →

The study examines how large language models (LLMs) respond to users in online political debates, finding that LLMs are more receptive to left‑leaning users than right‑leaning ones. This bias persists even when users defend the same position or appear together, and is influenced by user expertise and, to a lesser extent, rhetorical strategy. Fine‑tuning on different political corpora can reduce or reverse the effect, indicating that the bias is not inherent to the models but can be altered.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computation and Language.

Related stories

arXiv Computation and Language
Sep 25

Benchmarking Argumentative Behaviour of LLMs: A Study of Defences Against Character Attacks

The paper investigates how well large language models (LLMs) can handle character attacks—ad hominem arguments—in political debates. By analyzing natural political dialogues and comparing LLM-generated responses to a corpus of U.S. presidential debates, the study finds that most LLMs favor logical defenses and rarely use ethos-based counterattacks. The authors suggest that safety fine‑tuning limits LLMs’ strategic options, preventing them from fully engaging in realistic political discourse.

By Ewelina Gajewska, Katarzyna Budzynska, Jaroslaw Chudziak
llmsagentsfine-tuningbenchmarkssafety
More like this →
arXiv Computation and Language
Oct 19

High prevalence of antagonizing rhetoric in trending topics on Twitter/X

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
More like this →