The paper introduces a protocol for generating and evaluating codebooks for measuring Computational Discourse Quality Index (DQI) using Large Language Models (LLMs). It compares top‑down, bottom‑up, and hybrid approaches, finding that inductive, data‑driven schemas yield higher inter‑model agreement while remaining valid. Experiments on 100 Israeli parliament transcripts support the reliability of these data‑driven schemas for large‑scale political discourse analysis.
By Esther Shizgal, Ron Keinan, Daniel Shtauber, Roee Tzvi Esquira, Ori Aronson, Matan Toledano, Or Rappel-Kroyzer, Liron Lavi, Renana Keydar, Ittai Bar-Siman-Tov, Reut Tsarfaty
arXiv:2611.05381v1 Announce Type: new
Abstract: Legislative hearings are distinct communication events with their own human recognizable structure and logic, yet they have been analyzed as distinct p...
By Isaac Rudnick, Nipun Das, Foaad Khosmood
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.
By Mirko Tritella, Riccardo Pozzi, Matteo Palmonari
The paper introduces a transparent, hybrid framework for Quantitative Discourse Analysis that blends lexical and semantic techniques. Using custom Python pipelines with NLTK, spaCy, and Sentence Transformers, the authors fine‑tune preprocessing, lemmatisation, and embedding generation, then apply an iterative BERTopic workflow involving UMAP, HDBSCAN, and c‑TF‑IDF to produce coherent topics. The approach is illustrated through a historical political discourse case study, emphasizing code‑level transparency, researcher control, and methodological triangulation.
By Thomas Compton
The paper investigates how to detect enthymemes—arguments with unstated premises or conclusions—in controversial political tweets about vaccination and immigration. Despite extensive fine‑tuning, models achieve an F1 score of only about 0.67, prompting the authors to propose new evaluation metrics that compare predictions to the full distribution of annotator votes and a model that directly predicts this distribution. The study also shows that models rely on explicit argumentative structure, often missing implicit links that readers must infer, raising concerns for content moderation systems that depend on majority labels.
By Martial Pastor, Nelleke Oostdijk
Enthymemes, arguments with unstated premises or conclusions, are pervasive in persuasive discourse, yet their annotation remains notoriously subjective. We present a resource of 1,482 tweets from politically controversial discourse, annotated by five annotators for the presence of enthymemes and their argument structure, designed to study label variation.
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
arXiv:2508. 03250v4 Announce Type: replace-cross Abstract: The increasing amount of political debates and politics-related discussions calls for the definition of novel computational methods to automatically analyse such content with the final goal of lightening up political deliberation to citizens.
By Deborah Dore, Elena Cabrio, Serena Villata
arXiv:2607. 13260v1 Announce Type: cross Abstract: Policy documents shape governance outcomes, but their reasoning is often implicit.
By Stylianos Loukas Vasileiou, Olga Derendiaeva
arXiv:2608. 09925v1 Announce Type: cross Abstract: Large language models are increasingly being deployed in governmental settings, yet few existing evaluation frameworks jointly reflect the values of public administration and the linguistic requirements of non-English contexts.
By Laurens Samson, Iva Gornishka, Gossa L\^o, Yuki M. Asano, Sennay Ghebreab
arXiv:2610.33240v1 Announce Type: new
Abstract: As frontier AI systems increasingly serve as sources of political and current-events information, evaluating the epistemic quality of their responses i...
By Matt Wilde, Jillian Fisher, Andrew B. Hall, Kathryn Salam, Robbie Goldfarb
The paper presents a red‑teaming framework to evaluate how open‑source large language models (LLMs) can be used for political influence, focusing on models that can be deployed locally without weight modifications. It measures LLM Overton Windows (OWs) to assess expression fidelity of sampled political opinions and examines how natural‑language jailbreaks alter these scores. The study evaluates over 30 LLMs from 10 families across five countries, finding that left‑leaning content is expressed with higher fidelity, larger models tend to have lower mean OW scores, and jailbreak effectiveness varies significantly between model families.
By Daniel C. Ruiz, Anna Serbina, Ashwin Rao, Emilio Ferrara, Luca Luceri