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

Problem
The paper addresses the lack of large-scale, domain-specific datasets for stance detection in bioethical debates on social media, particularly on platforms like Reddit. Existing resources do not adequately capture the context-dependent nature of these discussions, which are critical for understanding public sentiment and argumentation in bioethics. This work is a preprint and has not undergone peer review.

Method
The authors present BioStance, a dataset comprising 39,600 annotated Post-Comment pairs derived from Reddit discussions on six bioethical controversies. The dataset is structured around three dimensions of bioethical conflict: fundamental value conflicts, individual liberty versus collective responsibility, and technological uncertainty. Each instance in the dataset maintains hierarchical conversational context, which is crucial for stance detection. Annotations were performed by three independent annotators using a three-class stance scheme: Favor, Against, and None. The inter-annotator agreement was quantified using Krippendorff’s alpha, achieving a mean score of 0.82, indicating substantial reliability in the annotations. The dataset’s design emphasizes thematic diversity and conversational structure, making it suitable for advanced research in context-aware stance detection and argument mining.

Results
While specific performance metrics on benchmark tasks are not provided in the abstract, the dataset’s construction and annotation quality suggest it will serve as a robust foundation for future models in stance detection. The authors imply that the dataset will enable improved performance in stance detection tasks compared to existing datasets, although direct comparisons with named baselines are not detailed in the provided text.

Limitations
The authors acknowledge that the dataset is limited to discussions on Reddit, which may not fully represent the broader spectrum of bioethical discourse across different platforms. Additionally, while the inter-annotator reliability is high, the reliance on a three-class stance scheme may oversimplify the complexity of opinions in bioethical discussions. The dataset’s focus on specific bioethical topics may also limit its generalizability to other domains of discourse.

Why it matters
The introduction of BioStance is significant for advancing research in context-aware stance detection, as it provides a rich resource for training and evaluating models that can understand nuanced arguments in bioethical discussions. This dataset can facilitate deeper computational analyses of public sentiment and argumentation strategies in bioethics, potentially influencing policy-making and public discourse. The work contributes to the growing field of argument mining and stance detection, as highlighted in related literature, and is available on arXiv.