Results 281 to 290 of about 4,967,798 (324)

EZ-STANCE: A Large Dataset for English Zero-Shot Stance Detection

Annual Meeting of the Association for Computational Linguistics
Zero-shot stance detection (ZSSD) aims to determine whether the author of a text is in favor, against, or neutral toward a target that is un-seen during training.
Chenye Zhao, Cornelia Caragea
semanticscholar   +1 more source

StanceEval 2024: The First Arabic Stance Detection Shared Task

ARABICNLP
Recently, there has been a growing interest in analyzing user-generated text to understand opinions expressed on social media. In NLP, this task is known as stance detection, where the goal is to predict whether the writer is in favor, against, or has no
N. Alturayeif, Hamzah Luqman, Zaid Alyafeai, Asma Yamani   +3 more
semanticscholar   +1 more source

Stanceformer: Target-Aware Transformer for Stance Detection

Conference on Empirical Methods in Natural Language Processing
The task of Stance Detection involves discerning the stance expressed in a text towards a specific subject or target. Prior works have relied on existing transformer models that lack the capability to prioritize targets effectively.
Krishna Garg, Cornelia Caragea
semanticscholar   +1 more source

A Survey of Stance Detection on Social Media: New Directions and Perspectives

arXiv.org
In modern digital environments, users frequently express opinions on contentious topics, providing a wealth of information on prevailing attitudes. The systematic analysis of these opinions offers valuable insights for decision-making in various sectors,
Bowen Zhang, Genan Dai, Fuqiang Niu, Nan Yin, Xiaomao Fan, Hutchin Huang   +5 more
semanticscholar   +1 more source

Efficient Stance Detection with Latent Feature

2017
Social platforms, such as Twitter, are becoming more and more popular. However it is hard to identify the sentimental stance from those social media. In this paper, an approach is proposed to identify the stance of opinion. Digging out the latent factors of the given rough processed information is essential because it has the potential to reveal ...
Xiaofei Xu, Fei Hu 0004, Peiwen Du, Jingyuan Wang, Li Li 0006   +4 more
openaire   +1 more source

EcoVerse: An Annotated Twitter Dataset for Eco-Relevance Classification, Environmental Impact Analysis, and Stance Detection

International Conference on Language Resources and Evaluation
Anthropogenic ecological crisis constitutes a significant challenge that all within the academy must urgently face, including the Natural Language Processing (NLP) community.
Francesca Grasso, Stefano Locci, G. Siragusa, Luigi Di Caro   +3 more
semanticscholar   +1 more source

EZ-STANCE: A Large Dataset for Zero-Shot Stance Detection

Findings of the Association for Computational Linguistics: EMNLP 2023, 2023
Chenye Zhao, Cornelia Caragea
openaire   +1 more source

Acquired TASTE: Multimodal Stance Detection with Textual and Structural Embeddings

arXiv.org
Stance detection plays a pivotal role in enabling an extensive range of downstream applications, from discourse parsing to tracing the spread of fake news and the denial of scientific facts.
Guy Barel, Oren Tsur, Dan Vilenchik
semanticscholar   +1 more source

MultiClimate: Multimodal Stance Detection on Climate Change Videos

NLP4PI
Climate change (CC) has attracted increasing attention in NLP in recent years. However, detecting the stance on CC in multimodal data is understudied and remains challenging due to a lack of reliable datasets.
Jiawen Wang, Longfei Zuo, Siyao Peng, Barbara Plank   +3 more
semanticscholar   +1 more source

Can Large Language Models Address Open-Target Stance Detection?

Annual Meeting of the Association for Computational Linguistics
Stance detection (SD) identifies the text position towards a target, typically labeled as favor, against, or none. We introduce Open-Target Stance Detection (OTSD), the most realistic task where targets are neither seen during training nor provided as ...
Abu Ubaida Akash, A. Fahmy, Amine Trabelsi   +2 more
semanticscholar   +1 more source