Generative data augmentation (GDA) has emerged as a promising technique to alleviate
data scarcity in machine learning applications. This thesis presents a comprehensive survey …

Identifying users' stances regarding specific targets/topics is a significant route to learning
public opinion from social media platforms. Most existing studies of stance detection strive to …

Generative data augmentation (GDA) has emerged as a promising technique to alleviate
data scarcity in machine learning applications. This thesis presents a comprehensive survey …

Stance detection is important for understanding different attitudes and beliefs on the Internet.
However, given that a passage's stance toward a given topic is often highly dependent on …

Multi-target stance detection is the detection of the stance of multiple targets in text.
Currently, most multi-target stance detection methods only detect the stance of two targets …

Zero-shot stance detection (ZSSD) identifies the stances of targets that have not been
encountered during the testing phase. Most of the existing efforts are dedicated to …

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 …

Zero-shot stance detection that aims to detect the stance (typically against, favor, or neutral)
towards unseen targets has attracted considerable attention. However, most previous …

Stance detection aims at inferring an author's attitude towards a specific target in a text. Prior
methods mainly consider target-related background information for a better understanding of …

Stance detection is a subproblem of sentiment analysis, commonly defined as classifying the
stance of a text toward a certain target:{Favor, Against, Neither}. As an important research …