DOI: 10.6084/m9.figshare.28327019
title: Dataset for "Neural embedding of beliefs reveals the role of relative dissonance in human decision-making".

description: This project contains the dataset used to generate the results of the study <b><i>"Neural embedding of beliefs reveals the role of relative dissonance in human decision-making"</i></b> (arXiv:2408.07237).<br>Authors: Byunghwee Lee<sup>1</sup>, Rachith Aiyappa<sup>1</sup>, Yong-Yeol Ahn<sup>1</sup>, Haewoon Kwak<sup>1</sup>, Jisun An<sup>1</sup><sup>1</sup> <sub>Center for Complex Networks and Systems Research, Luddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington, Indiana, USA, 47408</sub><br><b>DDO_dataset.zip (</b>original Debate.org dataset<b>)</b><br>This archive contains the original raw Debate.org dataset, which was obtained from the publicly accessible website (https://esdurmus.github.io/ddo.html), maintained by Esin Durmus [1,2]. <b>All credit for this dataset belongs entirely to the original authors</b><b>, Esin Durmus and Claire Cardie.</b> We do not claim any authorship or modifications to this dataset. It is provided here solely for reproducibility and reference in our study.<br><br>The dataset includes the following three files:<br><br>- <b>debates.json</b>: This JSON file contains a Python dictionary that assigns a <i>debate name</i> --- a unique name for each debate --- to debate information<br>- <b>users.json</b>: This JSON file includes a Python dictionary containing user information<br>- <b>readme.md</b> file from the authors (Esin Durmus and Claire Cardie)<br><br>When using this dataset, please reference Debate.org and cite the following works:<br><br>[1] Esin Durmus and Claire Cardie. 2019. A Corpus for Modeling User and Language Effects in Argumentation on Online Debating. <i>In Proceedings of the 57th Conference of the Association for Computational Linguistics</i>. Florence, Italy. Association for Computational Linguistics.<br><br>[2] Esin Durmus and Claire Cardie. 2018. Exploring the Role of Prior Beliefs for Argument Persuasion. <i>In Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL).</i><br><b>df_ddo_including_only_truebeliefs_nodup(N192307).p</b><br>This file contains a pre-processed dataset used in our project (arXiv:2408.07237). The dataset includes records of user participation in debates (both as debaters and voters) as well as voting records across various debates. The belief triplet dataset used for fine-tuning a Sentence-BERT model was generated based on this pre-processed dataset. Detailed explanations of the pre-processing procedure are provided in the Methods section of the paper.<br><br>When using this pre-processed dataset, please cite the following reference (in addition to the two papers mentioned above):<br><br>[3] Lee, B., Aiyappa, R., Ahn, Y. Y., Kwak, H., & An, J. (2024). <i>Neural embedding of beliefs reveals the role of relative dissonance in human decision-making.</i> arXiv preprint arXiv:2408.07237.<br><br><b>model_full_data.zip</b><br>This zip file contains five fine-tuned S-BERT models trained using a 5-fold belief triplet dataset. After unzipping the files, users can import the models using the 'sentence_transformers' Python library (https://sbert.net/).<br>