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.

We consider how to credibly and reliably assess the opinions of individuals using their social media posts. To this end, this paper makes three contributions. First, we assemble a workflow and approach to applying modern natural language processing (NLP) methods to multi-target user stance detection in the wild. Second, we establish why the multi-target modeling of user stance is qualitatively more complicated than uni-target user-stance detection. Finally, we validate our method by showing how multi-dimensional measurement of user opinions not only reproduces known opinion polling results, but also enables the study of opinion dynamics at high levels of temporal and semantic resolution.