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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.
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...
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.
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.
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.
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.