The study examines how the use of evidence-oriented versus intuition-oriented language—measured by the Evidence‑Minus‑Intuition (EMI) score—varies among individual U.S. Congress members and relates to their legislative effectiveness. It finds that more ideologically extreme legislators tend to use less evidence-oriented language on the floor, that EMI scores are consistent across floor speeches and Twitter posts (though lower on Twitter overall), and that higher EMI scores on the floor predict greater legislative effectiveness even after controlling for ideology and other factors. The research highlights evidence‑based communication as a significant individual attribute linked to legislative success.
arXiv:2609.10198v1 Announce Type: new Abstract: Emotions are a pervasive feature of political communication, yet existing research has focused primarily on describing patterns of emotional expression...
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.
arXiv:2607. 20487v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used to answer questions about political information, including in election-adjacent information settings where factual errors and ideological distortions are high-stakes.
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...
The study analyzes oral political language in U.S. presidential debates from 1960 to 2024, focusing on 19 candidates. It finds a clear trend toward simplification: sentence length and complex terms have decreased, while emotional tone has risen and logical, rational content has diminished. The research also explores whether specific presidents exhibit unique stylistic traits and whether language patterns correlate with electoral success.
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...
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...
arXiv:2608. 13410v1 Announce Type: new Abstract: Parliamentary proceedings are a primary record of democratic deliberation, yet their volume and fragmentation make multi-perspective access difficult for citizens, journalists, and researchers.
arXiv:2604. 20711v2 Announce Type: replace Abstract: Artificial intelligence is increasingly deployed to synthesize large-scale public input in policy consultations and participatory processes.
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...
As users increasingly turn to Large Language Models (LLMs) for information and advice on political matters, particularly during election periods, the political preferences expressed by these systems have become a matter of public interest. Prior research has shown that interactions with LLMs can influence users' political attitudes and choices, raising questions about how these models themselves evaluate political actors.
arXiv:2603. 23841v2 Announce Type: replace-cross Abstract: While Large Language Models (LLMs) are increasingly used as primary sources of information, their potential for political bias may impact their objectivity.