Stance Quantification: Definition of the Problem

TL;DR

This paper introduces stance quantification, a new research problem that measures the proportion of texts favoring, opposing, or neutral towards a target, with potential applications in privacy-sensitive contexts.

Contribution

It formally defines stance quantification and its multi-target variant, providing a new framework for aggregate stance analysis beyond traditional stance detection.

Findings

Defines stance quantification and multi-target stance quantification.

Highlights potential applications in privacy-sensitive environments.

Suggests stance quantification as a novel approach for aggregate stance analysis.

Abstract

Stance detection is commonly defined as the automatic process of determining the positions of text producers, towards a target. In this paper, we define a research problem closely related to stance detection, namely, stance quantification, for the first time. We define stance quantification on a pair including (1) a set of natural language text items and (2) a target. At the end of the stance quantification process, a triple is obtained which consists of the percentages of the number of text items classified as Favor, Against, Neither, respectively, towards the target in the input pair. Also defined in the current paper is a significant subproblem of the stance quantification problem, namely, multi-target stance quantification. We believe that stance quantification at the aggregate level can lead to fruitful results in many application settings, and furthermore, stance quantification might be the sole stance related analysis alternative in settings where privacy concerns prevent researchers from applying generic stance detection.