Go to ACL ARR 2025 May homepageCorpus-Oriented Stance Target Extraction
Abstract: Understanding public discourse through the frame of stance detection requires effective extraction of issues of discussion, or stance targets. Yet current approaches to stance target extraction are limited, only focusing on a single document to single stance target mapping. We propose a broader view of stance target extraction, which we call corpus-oriented stance target extraction. This approach considers that documents have multiple stance targets, those stance targets are hierarchical in nature, and document stance targets should not be considered in isolation of other documents in a corpus. We develop a formalization and metrics for this task, propose a new method to address this task, and show its improvement over previous methods using supervised and unsupervised metrics, and human evaluation tasks. Finally, we demonstrate its utility in a case study, showcasing its ability to aid in reliably surfacing key issues of discussion in large-scale corpuses.
Paper Type: Long
Research Area: Computational Social Science and Cultural Analytics
Research Area Keywords: stance detection, NLP tools for social analysis
Contribution Types: NLP engineering experiment
Languages Studied: english
Submission Number: 4679
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