Dialogin dyynit

  Automated Optimization of Argumentative Quality in Audio Content Research article generated from the publication-ready master prompt A literature-driven blueprint for computational argumentation, audio NLP, and recommendation-system design Date: 13 March 2026 Abstract. This document turns the prior publication-level prompt into a full research-style article. It proposes a rigorous framework for measuring and optimizing argumentative quality in audio content such as podcasts, debates, and talk programs. The framework integrates argumentation theory, multimodal signal processing, large language model prompting, fallacy and scheme detection, and recommendation-system design. It introduces an operational Argument Quality Index (AQI), JSON output schemas for model pipelines, and an evaluation protocol that combines component detection, ranking quality, calibration, and human review. The document is anchored in recent work on podcast argument mini...

  Cognitive Heuristics in Audio Content Selection A Behavioral and Computational Analysis of Podcast Consumption Document type Research article draft Methodological orientation Cognitive psychology, media choice theory, and computational social science Prepared with Structured synthesis based on the supplied master prompt   This paper presents a theory-driven research article with a hypothetical empirical design and simulated findings.   Abstract This article examines how listeners choose podcasts, audiobooks, and adjacent forms of audio media under conditions of limited attention and abundant supply. The central argument is that audio-content selection is governed less by deliberate comparison of alternatives than by fast, low-cost heuristics that exploit familiarity, authority, topical salience, identity congruence, and cognitive ease. The paper integrates the heuristics-an...