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BioStance introduces a specialized dataset designed to improve stance detection models within the complex domain of bioethical discussions on Reddit. By preserving hierarchical conversational context across six controversial targets, it addresses current gaps in modeling nuanced value conflicts for argument mining tasks. This resource offers researchers high-quality annotations with substantial inter-annotator reliability specifically tailored for NLP applications in sensitive social discourse.
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A Context-Aware Dataset for Stance Detection in Bioethical Controversies on Reddit

arXiv2 min read

arXiv:2606.13187v1 Announce Type: new 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 $\alpha$ 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.

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 $\alpha$ 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.

Computation and Language (cs.CL)

arXiv:2606.13187 [cs.CL]

(or arXiv:2606.13187v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2606.13187 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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Full text under the publisher's open licence · 211 words

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