Dataset for "Neural embedding of beliefs reveals the role of relative dissonance in human decision-making".

This project contains the dataset used to generate the results of the study "Neural embedding of beliefs reveals the role of relative dissonance in human decision-making" (arXiv:2408.07237).Authors: Byunghwee Lee1, Rachith Aiyappa1, Yong-Yeol Ahn1, Haewoon Kwak1, Jisun An11 Center for Complex Networks and Systems Research, Luddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington, Indiana, USA, 47408DDO_dataset.zip (original Debate.org dataset)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]. All credit for this dataset belongs entirely to the original authors, Esin Durmus and Claire Cardie. We do not claim any authorship or modifications to this dataset. It is provided here solely for reproducibility and reference in our study.The dataset includes the following three files:- debates.json: This JSON file contains a Python dictionary that assigns a debate name --- a unique name for each debate --- to debate information- users.json: This JSON file includes a Python dictionary containing user information- readme.md file from the authors (Esin Durmus and Claire Cardie)When using this dataset, please reference Debate.org and cite the following works:[1] Esin Durmus and Claire Cardie. 2019. A Corpus for Modeling User and Language Effects in Argumentation on Online Debating. In Proceedings of the 57th Conference of the Association for Computational Linguistics. Florence, Italy. Association for Computational Linguistics.[2] Esin Durmus and Claire Cardie. 2018. Exploring the Role of Prior Beliefs for Argument Persuasion. In Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL).df_ddo_including_only_truebeliefs_nodup(N192307).pThis 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.When using this pre-processed dataset, please cite the following reference (in addition to the two papers mentioned above):[3] Lee, B., Aiyappa, R., Ahn, Y. Y., Kwak, H., & An, J. (2024). Neural embedding of beliefs reveals the role of relative dissonance in human decision-making. arXiv preprint arXiv:2408.07237.model_full_data.zipThis 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/).