Cross-target stance detection: A survey of techniques, datasets, and challenges

Abstract

Stance detection is the task of determining the viewpoint expressed in a text towards a given target. A specific direction within the task focuses on cross-target stance detection, where a model trained on samples pertaining to certain targets is then applied to a new, unseen target. With the increasing need to analyze and mine viewpoints and opinions online, the task has recently seen a significant surge in interest. This review paper examines research in cross-target stance detection over the last decade, highlighting the evolution from basic statistical methods to contemporary neural and LLM-based models. All this research has led to notable improvements in accuracy and adaptability. Innovative approaches include the use of topic-grouped attention and adversarial learning for zero-shot detection, as well as fine-tuning techniques that enhance model robustness. Additionally, prompt-tuning methods and the integration of external knowledge have further refined model performance. A comprehensive overview of the datasets used for evaluating these models is also provided, offering valuable insights into the progress and challenges in the field. We conclude by highlighting emerging research directions and suggesting avenues for future work. • We present a comprehensive review of cross-target stance detection methods. • We highlight recent progress and remaining challenges in cross-target stance detection. • We analyze datasets for cross-target stance detection, outlining strengths and limits. • We offer insights into future research directions for cross-target stance detection.

Topics & Concepts