# Capturing Opinion Shifts in Deliberative Discourse through Frequency-based Quantum deep learning methods

arXiv: https://arxiv.org/abs/2509.22603 · HTML: https://mlsanity.com/paper/2509.22603/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 8 references that are in the map.
- First posted on arXiv 26 Sep 2025.

## Builds on (most cited first, 5 of 5)

- [Winning Arguments: Interaction Dynamics and Persuasion Strategies in Good-faith Online Discussions](https://mlsanity.com/paper/1602.01103/) (2016)
- [Trouble on the Horizon: Forecasting the Derailment of Online Conversations as they Develop](https://mlsanity.com/paper/1909.01362/) (2019)
- [DeliData: A dataset for deliberation in multi-party problem solving](https://mlsanity.com/paper/2108.05271/) (2021)
- [I Beg to Differ: A study of constructive disagreement in online conversations](https://mlsanity.com/paper/2101.10917/) (2021)
- [Machine Learning for Enhancing Deliberation in Online Political Discussions and Participatory Processes: A Survey](https://mlsanity.com/paper/2506.02533/) (2025)

## Abstract (arXiv)

Deliberation plays a crucial role in shaping outcomes by weighing diverse perspectives before reaching decisions. With recent advancements in Natural Language Processing, it has become possible to computationally model deliberation by analyzing opinion shifts and predicting potential outcomes under varying scenarios. In this study, we present a comparative analysis of multiple NLP techniques to evaluate how effectively models interpret deliberative discourse and produce meaningful insights. Opinions from individuals of varied backgrounds were collected to construct a self-sourced dataset that reflects diverse viewpoints. Deliberation was simulated using product presentations enriched with striking facts, which often prompted measurable shifts in audience opinions. We have given comparative analysis between two models namely Frequency-Based Discourse Modulation and Quantum-Deliberation Framework which outperform the existing state of art models. The findings highlight practical applications in public policy-making, debate evaluation, decision-support frameworks, and large-scale social media opinion mining.