Results 271 to 280 of about 4,967,798 (324)

Disentangled VAD Representations via a Variational Framework for Political Stance Detection

arXiv.org
The stance detection task aims to categorise the stance regarding specified targets. Current methods face challenges in effectively integrating sentiment information for stance detection.
Beiyu Xu, Zhiwei Liu, Sophia Ananiadou
semanticscholar   +1 more source

A Challenge Dataset and Effective Models for Conversational Stance Detection

International Conference on Language Resources and Evaluation
Previous stance detection studies typically concentrate on evaluating stances within individual instances, thereby exhibiting limitations in effectively modeling multi-party discussions concerning the same specific topic, as naturally transpire in ...
Fuqiang Niu, Min Yang, Ang Li, Baoquan Zhang, Xiaojiang Peng, Bowen Zhang   +5 more
semanticscholar   +1 more source

Tree-of-Counterfactual Prompting for Zero-Shot Stance Detection

Annual Meeting of the Association for Computational Linguistics
Stance detection enables the inference of atti-001 tudes from human communications. Automatic 002 stance identification was mostly cast as a classi-003 fication problem. However, stance decisions in-004 volve complex judgments, which can be nowa-005 days
Maxwell Weinzierl, S. Harabagiu
semanticscholar   +1 more source

Multimodal Multi-turn Conversation Stance Detection: A Challenge Dataset and Effective Model

ACM Multimedia
Stance detection, which aims to identify public opinion towards specific targets using social media data, is an important yet challenging task. With the proliferation of diverse multimodal social media content including text, and images multimodal stance
Fuqiang Niu, Zebang Cheng, Xianghua Fu, Xiaojiang Peng, Genan Dai, Yin Chen, Hutchin Huang, Bowen Zhang   +7 more
semanticscholar   +1 more source

Stance Detection on Social Media with Fine-Tuned Large Language Models

arXiv.org
Stance detection, a key task in natural language processing, determines an author's viewpoint based on textual analysis. This study evaluates the evolution of stance detection methods, transitioning from early machine learning approaches to the ...
Ilker Gul, R. Lebret, K. Aberer
semanticscholar   +1 more source

Social media user stance detection without stance text

Anais do XXI Simpósio Brasileiro de Sistemas de Informação (SBSI 2025)
Context: User stance detection systems are intended to determine whether an individual holds a stance for or against a given target topic. For instance, ‘Vaccines save lives’ is a stance in favour of vaccination. Problem: Most existing approaches to stance detection take as an input a piece of text assumed to convey a stance towards the intended target,
Pedro Semcovici, Ivandré Paraboni
openaire   +1 more source

Stance Reasoner: Zero-Shot Stance Detection on Social Media with Explicit Reasoning

International Conference on Language Resources and Evaluation
Social media platforms are rich sources of opinionated content. Stance detection allows the automatic extraction of users’ opinions on various topics from such content.
Maksym Taranukhin, V. Shwartz, E. Milios
semanticscholar   +1 more source

DEEM: Dynamic Experienced Expert Modeling for Stance Detection

International Conference on Language Resources and Evaluation
Recent work has made a preliminary attempt to use large language models (LLMs) to solve the stance detection task, showing promising results. However, considering that stance detection usually requires detailed background knowledge, the vanilla reasoning
Xiaolong Wang, Yile Wang, Sijie Cheng, Peng Li, Yang Liu   +4 more
semanticscholar   +1 more source

Large Language Model Enhanced Logic Tensor Network for Stance Detection

Neural Networks
Social media platforms, rich in user-generated content, offer a unique perspective on public opinion, making stance detection an essential task in opinion mining.
Genan Dai, Jiayu Liao, Sicheng Zhao, Xianghua Fu, Xiaojiang Peng, Hutchin Huang, Bowen Zhang   +6 more
semanticscholar   +1 more source

The Power of LLM-Generated Synthetic Data for Stance Detection in Online Political Discussions

International Conference on Learning Representations
Stance detection holds great potential to improve online political discussions through its deployment in discussion platforms for purposes such as content moderation, topic summarization or to facilitate more balanced discussions.
Stefan Sylvius Wagner, Maike Behrendt, Marc Ziegele, Stefan Harmeling   +3 more
semanticscholar   +1 more source