5 citations · 12 across the 15 of their papers we have counts for
16 papers
TikStance: A Multimodal and Hierarchical Dataset for Multi-target Stance Analysis in TikTok Political Conversations
Yazhi Zhang, Fuqiang Niu, Bowen Zhang
Political discourse has increasingly moved to short-video platforms, yet computational analysis of such content remains constrained by the scarcity of datasets that jointly preserv…
SICI: A Semantic-Pragmatic Complexity Index Reveals Regime Shifts in LLM Stance Detection
Fuqiang Niu, Bowen Zhang
Prompt-based LLMs are increasingly used for stance detection, but harder examples are not always repaired by clearer instructions, reasoning prompts, retrieval, or debate. We intro…
ARMOR-MAD: Adaptive Routing for Heterogeneous Multi-Agent Debate in Large Language Model Reasoning
Fuqiang Niu, Bowen Zhang
Multi-agent debate (MAD) can improve large language model reasoning, but fixed debate pipelines often waste computation and can amplify correlated errors among similar agents. We p…
A Context-Aware Dataset for Stance Detection in Bioethical Controversies on Reddit
Hu Huang, Genan Dai, Fuqiang Niu +3
Bioethical debates increasingly unfold on social media, yet stance detection research lacks large-scale, domain-specific resources for modeling such context-dependent discourse. We…
Rethinking Fusion: Disentangled Learning of Shared and Modality-Specific Information for Stance Detection
Zhiyu Xie, Fuqiang Niu, Genan Dai +4
Multi-modal stance detection (MSD) aims to determine an author's stance toward a given target using both textual and visual content. While recent methods leverage multi-modal fusio…
TwiUSD: A Benchmark Dataset and Structure-Aware LLM Framework for User Stance Detection
Fuqiang Niu, Zini Chen, Zhiyu Xie +3
User-level stance detection (UserSD) remains challenging due to the lack of high-quality benchmarks that jointly capture linguistic and social structure. In this paper, we introduc…
MT2-CSD: A New Dataset and Multi-Semantic Knowledge Fusion Method for Conversational Stance Detection
Fuqiang Niu, Genan Dai, Yisha Lu +4
In the realm of contemporary social media, automatic stance detection is pivotal for opinion mining, as it synthesizes and examines user perspectives on contentious topics to uncov…
Induce, Align, Predict: Zero-Shot Stance Detection via Cognitive Inductive Reasoning
Bowen Zhang, Jun Ma, Fuqiang Niu +3
Zero-shot stance detection (ZSSD) seeks to determine the stance of text toward previously unseen targets, a task critical for analyzing dynamic and polarized online discourse with…
C-MTCSD: A Chinese Multi-Turn Conversational Stance Detection Dataset
Fuqiang Niu, Yi Yang, Xianghua Fu +2
Stance detection has become an essential tool for analyzing public discussions on social media. Current methods face significant challenges, particularly in Chinese language proces…
DualCoTs: Dual Chain-of-Thoughts Prompting for Sentiment Lexicon Expansion of Idioms
Fuqiang Niu, Minghuan Tan, Bowen Zhang +2
Idioms represent a ubiquitous vehicle for conveying sentiments in the realm of everyday discourse, rendering the nuanced analysis of idiom sentiment crucial for a comprehensive und…
A Survey of Stance Detection on Social Media: New Directions and Perspectives
Bowen Zhang, Genan Dai, Fuqiang Niu +5
In modern digital environments, users frequently express opinions on contentious topics, providing a wealth of information on prevailing attitudes. The systematic analysis of these…
Multimodal Multi-turn Conversation Stance Detection: A Challenge Dataset and Effective Model
Fuqiang Niu, Zebang Cheng, Xianghua Fu +5
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 mu…
MIPS at SemEval-2024 Task 3: Multimodal Emotion-Cause Pair Extraction in Conversations with Multimodal Language Models
Zebang Cheng, Fuqiang Niu, Yuxiang Lin +3
This paper presents our winning submission to Subtask 2 of SemEval 2024 Task 3 on multimodal emotion cause analysis in conversations. We propose a novel Multimodal Emotion Recognit…
A Challenge Dataset and Effective Models for Conversational Stance Detection
Fuqiang Niu, Min Yang, Ang Li +3
Previous stance detection studies typically concentrate on evaluating stances within individual instances, thereby exhibiting limitations in effectively modeling multi-party discus…
MoZIP: A Multilingual Benchmark to Evaluate Large Language Models in Intellectual Property
Shiwen Ni, Minghuan Tan, Yuelin Bai +9
Large language models (LLMs) have demonstrated impressive performance in various natural language processing (NLP) tasks. However, there is limited understanding of how well LLMs p…
A Logically Consistent Chain-of-Thought Approach for Stance Detection
Bowen Zhang, Daijun Ding, Liwen Jing +1
Zero-shot stance detection (ZSSD) aims to detect stances toward unseen targets. Incorporating background knowledge to enhance transferability between seen and unseen targets consti…