A Comparative Analysis of Stance Detection Approaches and Datasets
Parush Gera, Tempestt J. Neal · 2022
Various approaches have been proposed for automated stance detection, including those that use machine and deep learning models and natural language processing techniques.However, their cross-dataset performance, the impact of sample size on performance, and experimental aspects such as runtime have yet to be compared, limiting what is known about the generalizability of prominent approaches.This paper presents a replication study of stance detection approaches on current benchmark datasets.Specifically, we compare six existing machine and deep learning stance detection models on three publicly available datasets.We investigate performance as a function of the number of samples, length of samples (word count), representation across targets, type of text data, and the stance detection models themselves.We identify the current limitations of these approaches and categorize their utility for stance detection under varying circumstances (e.g., size of text samples), which provides valuable insight for future research in stance detection.