A Context-Aware Dataset for Stance Detection in Bioethical Controversies on Reddit

2026-06-11

AI Summary

This study addresses the growing prevalence of bioethical controversies on social media and the lack of large-scale, domain-specific resources that model conversational context for stance detection. To bridge this gap, the authors introduce BioStance, a novel dataset comprising 39,600 annotated post–comment pairs from Reddit, spanning six contentious topics and three bioethical dimensions. The dataset preserves hierarchical dialogue structure and employs a three-way annotation scheme (support, oppose, neutral). High inter-annotator agreement, with an average Krippendorff’s α of 0.82, attests to its reliability. BioStance constitutes the first benchmark for bioethical stance detection that integrates thematic diversity, contextual dialogue modeling, and high-quality human annotations, thereby offering a foundational resource for research in computational bioethics and argument mining.

Abstract

Bioethical debates increasingly unfold on social media, yet stance detection research lacks large-scale, domain-specific resources for modeling such context-dependent discourse. We present BioStance, a context-aware dataset of 39,600 annotated Post-Comment pairs from Reddit bioethical discussions. BioStance covers six controversial targets across three dimensions of bioethical controversy: fundamental value conflicts, individual liberty versus collective responsibility, and technological uncertainty. Each instance preserves hierarchical conversational context and is labeled by three independent annotators using a three-class stance scheme: Favor, Against, and None. The annotations achieve a mean Krippendorff's $α$ of 0.82, indicating substantial reliability. By combining thematic diversity, conversational structure, and high-quality human annotation, BioStance supports research on context-aware stance detection, argument mining, and computational analysis of bioethical discourse.

Problem

• stance detection
• bioethical controversies
• context-aware dataset
• social media discourse
• computational ethics

Innovation

• stance detection
• context-aware dataset
• bioethical controversy
• argument mining
• social media discourse