Zero-shot stance detection: Paradigms and challenges [PDF]
Frontiers in Artificial Intelligence, 2023 A major challenge in stance detection is the large (potentially infinite) and diverse set of stance topics. Collecting data for such a set is unrealistic due to both the expense of annotation and the continuous creation of new real-world topics (e.g., a ...
Emily Allaway, Kathleen McKeown
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Zero-shot cross-lingual stance detection via adversarial language adaptation [PDF]
PeerJ Computer ScienceStance detection has been widely studied as the task of determining if a social media post is positive, negative or neutral towards a specific issue, such as support towards vaccines.
Bharathi A., Arkaitz Zubiaga
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Constructing and evaluating ArabicStanceX: a social media dataset for Arabic stance detection [PDF]
Frontiers in Artificial IntelligenceArabic stance detection has attracted significant interest due to the growing importance of social media in shaping public opinion. However, the lack of comprehensive datasets has limited research progress in Arabic Natural Language Processing (NLP).
Ali Alkhathlan +3 more
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Social context in political stance detection: Impact and extrapolation. [PDF]
PLoS ONEStance detection is an important task with a wide range of high-impact social applications, including opinion polling and detecting propaganda, misinformation, and hate speech. In this work, we explore the performance and extrapolation power of political
Ramon Villa-Cox +2 more
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LOGIC: LLM-originated guidance for internal cognitive improvement of small language models in stance detection [PDF]
PeerJ Computer ScienceStance detection is a critical task in natural language processing that determines an author’s viewpoint toward a specific target, playing a pivotal role in social science research and various applications.
Woojin Lee, Jaewook Lee, Harksoo Kim
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Evaluating automatic annotation of lexicon-based models for stance detection of M-pox tweets from May 1st to Sep 5th, 2022. [PDF]
PLOS Digital HealthManually labeling data for supervised learning is time and energy consuming; therefore, lexicon-based models such as VADER and TextBlob are used to automatically label data.
Nicholas Perikli +10 more
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Collaborative Knowledge Infusion for Low-Resource Stance Detection
Big Data Mining and AnalyticsStance detection is the view towards a specific target by a given context (e.g. tweets, commercial reviews). Target-related knowledge is often needed to assist stance detection models in understanding the target well and making detection correctly ...
Ming Yan +2 more
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A systematic review of machine learning techniques for stance detection and its applications. [PDF]
Neural Comput Appl, 2023 Alturayeif N, Luqman H, Ahmed M.
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Stance Detection Based on User Connection [PDF]
Jisuanji kexue, 2022 The main purpose of stance detection is to mine users’ attitude towards topics or events.Different from other text classification tasks,the expression about stance is more obscure,and the attitude is more sensitive to users.The current stance detection ...
LI Zi-yi, ZHOU Xia-bing, WANG Zhong-qing, ZHANG Min
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International Journal of Computational Intelligence Systems, 2023 Zero-shot stance detection is both crucial and challenging because it demands detecting the stances of previously unseen targets in the inference stage.
Yu Zhang, Chunling Wang, Jia Wang
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