Results 261 to 270 of about 4,967,798 (324)

LLM-Driven Knowledge Injection Advances Zero-Shot and Cross-Target Stance Detection

North American Chapter of the Association for Computational Linguistics
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 targets while neglecting the accompanying input texts.
Zhao Zhang, Yiming Li, Jin Zhang, Hui Xu
semanticscholar   +1 more source

Controversy Detection and Stance Analysis

Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2015
Alerting users about controversial search results can encourage critical literacy, promote healthy civic discourse and counteract the "filter bubble" effect. Additionally, presenting information to the user about the different stances or sides of the debate can help her navigate the landscape of search results.
openaire   +1 more source

Stance Detection Based on Ensembles of Classifiers

Programming and Computer Software, 2019
A method of stance detection in text is proposed. This method is based on the machine learning of ensembles of classifiers. It is known that ensembles have advantages over individual classifiers, which often improves the quality of classification. An important issue is determining the classifiers that should be included in such an ensemble.
Sergey V. Vychegzhanin, Evgeny V. Kotelnikov   +1 more
openaire   +1 more source

C-MTCSD: A Chinese Multi-Turn Conversational Stance Detection Dataset

The Web Conference
Stance detection has become an essential tool for analyzing public discussions on social media. Current methods face significant challenges, particularly in Chinese language processing and multi-turn conversational analysis. To address these limitations,
Fuqiang Niu, Yi Yang, Xianghua Fu, Genan Dai, Bowen Zhang   +4 more
semanticscholar   +1 more source

Exploring Vision Language Models for Multimodal and Multilingual Stance Detection

arXiv.org
Social media's global reach amplifies the spread of information, highlighting the need for robust Natural Language Processing tasks like stance detection across languages and modalities.
Jake Vasilakes, Carolina Scarton, Zhixue Zhao   +2 more
semanticscholar   +1 more source

Limited Effectiveness of LLM-based Data Augmentation for COVID-19 Misinformation Stance Detection

The Web Conference
Misinformation surrounding emerging outbreaks poses a serious societal threat, making robust countermeasures essential. One promising approach is stance detection (SD), which identifies whether social media posts support or oppose misleading claims.
Eun Cheol Choi, Ashwin Balasubramanian, Jinhu Qi, Emilio Ferrara   +3 more
semanticscholar   +1 more source

Zero-shot Stance Detection with Logically Consistent Data Augmentation

IEEE International Conference on Acoustics, Speech, and Signal Processing
Zero-shot stance detection (ZSSD) is a challenging task that requires classifying stances towards unseen targets without large, well-curated training datasets.
Bowen Zhang, Xu Li, Jun Ma, Xi Zhang, Genan Dai, Jianhua Ye   +5 more
semanticscholar   +1 more source

Stance Identification by Sentiment and Target Detection

2020 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT), 2020
Stance detection has attracted attention for several years. Previous work focuses mainly on a supervised topic-specific setting which requires labeled data for each individual topic. In this paper, we discuss the characteristics of different types of topics, and the interaction among sentiment, target, and stance in a sentence.
Chiao-Chen Chen, Hsin-Yu Tsai, Hen-Hsen Huang, Hsin-Hsi Chen   +3 more
openaire   +1 more source

A Stance Detection Model Based on Sentiment Analysis and Toxic Language Detection

Electronics
In this paper, we present a stance detection model grounded in multi-task learning, specifically designed to address the intricate challenge of text stance analysis within social media comments.
Long Kang, Jiaqi Yao, Ruoshuang Du, Lu Ren, Haifeng Liu, Bo Xu   +5 more
semanticscholar   +1 more source

Emotion-aware dual cross-attentive neural network with label fusion for stance detection in misinformative social media content

Engineering applications of artificial intelligence
The rapid evolution of social media has generated an overwhelming volume of user-generated content, conveying implicit opinions and contributing to the spread of misinformation.
Lata Pangtey, M. Z. Rehman, Prasad Chaudhari, Shubhi Bansal, Nagendra Kumar   +4 more
semanticscholar   +1 more source