Disentangling conviction and conformity: a Bayesian ideal point model of voting behaviour in online debates

Official repository for the paper:

Disentangling conviction and conformity: a Bayesian ideal point model of voting behaviour in online debates
Elena Candellone

Overview

This repository contains the code, data processing scripts, and analysis pipeline used in the paper.

Abstract

Online debate platforms offer a unique window into the mechanisms driving opinion formation: they capture both explicit political preferences and the peer environment in which those preferences are expressed. In this work, I develop a Bayesian logistic regression model, inspired by ideal point models from political science, to disentangle two competing mechanisms of voting behaviour in online debates: conviction, driven by prior ideological beliefs, and conformity, driven by peer influence. I apply this framework to the Debate.org dataset, comprising approximately 341k votes across 78k debates on 48 socio-political topics. As the debate platform does not provide predefined topic labels for each debate, I infer the topic and stance from the debate text using large language models, and, with a Bayesian approach, I quantify the relative contribution of each mechanism. I find substantial heterogeneity across topics: conviction dominates on issues tied to personal freedoms and lifestyle choices, such as drug legalisation and legalised prostitution, while conformity dominates on several topics widely regarded as paradigmatic cases of moral conviction, including abortion, gun rights, and global warming. These results have implications for the stability of online political discourse and the design of deliberative platforms.

Repository Structure

project/
├── scripts/                           # Analysis scripts
    ├── 1_data_cleaning.py             # Data processing from raw data
    ├── 2x_debate_classification_x.py  # Classification of debates with gpt or bert
    ├── 3_user_vectors.py              # Generate user vectors from self-reported beliefs
    ├── 4x_debate_vectors_x.py         # Generate debate vectors from classification (gpt or bert)
    ├── 5x_run_model_x.py              # Run Bayesian model with Stan (gpt or bert)
    └── model.stan                     # Stan code containing the model
├── src/                               # Imports and additional scripts
├── results/                           # Generated results
├── plots/                             # Figures used in the manuscript
├── ddo.yml                            # Conda environment
└── README.md

Installation

Clone the repository

git clone https://github.com/elenacandellone/DDO-conviction-conformity.git
cd DDO-conviction-conformity

Create environment

Using Conda:

conda create --name ddo --file ddo.yaml
conda activate ddo

Data

Access

Describe where the data comes from.

• Public dataset: Dataset Name
• Processed data used in the paper: data/processed/

Reproducing preprocessing

python scripts/1_data_cleaning.py

Reproducing Results

Main experiments

python scripts/5a_run_model_gpt.py

Generate figures

b_figures.ipynb

Citation

If you use this code/paper, please cite:

@misc{candellone2026disentangling,
    title={Disentangling conviction and conformity: a Bayesian ideal point model of voting behaviour in online debates}, 
    author={Elena Candellone},
    year={2026},
    eprint={2606.03786},
    archivePrefix={arXiv},
    primaryClass={physics.soc-ph},
    url={https://arxiv.org/abs/2606.03786}, 
}

License

This project is released under the MIT License. See LICENSE for details.

Contact

For questions regarding the code or paper:

• Elena Candellone
• e.candellone@uu.nl