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Chunyan Hou

6 accepted papers

2025

FGDGNN: Fine-Grained Dynamic Graph Neural Network for Rumor Detection on Social Media

ACL 2025finding

Detecting rumors on social media has become a crucial issue.Propagation structure-based methods have recently attracted increasing attention.When the propagation structure is represented by the dynamic graph, temporal information is considered.However, existing rumor detection models using dynamic g…

Cited by 0SourcePDFScholar
2025

InstructGEC: Enhancing Unsupervised Grammatical Error Correction with Instruction Tuning

COLING 2025main

Recent works have proposed methods of generating synthetic data automatically for unsupervised Grammatical Error Correction (GEC). Although a large amount of synthetic data is generated at a low cost, it is unrealistic and of poor quality. The copying phenomenon of synthetic data prevents GEC models…

2025

SWAM: Adaptive Sliding Window and Memory-Augmented Attention Model for Rumor Detection

EMNLP 2025

Detecting rumors on social media has become a critical task in combating misinformation. Existing propagation-based rumor detection methods often focus on the static propagation graph, overlooking that rumor propagation is inherently dynamic and incremental in the real world. Recently propagation-ba

Cited by 0SourcePDFScholar
2025

SafeInt: Shielding Large Language Models from Jailbreak Attacks via Safety-Aware Representation Intervention

EMNLP 2025

With the widespread real-world deployment of large language models (LLMs), ensuring their behavior complies with safety standards has become crucial. Jailbreak attacks exploit vulnerabilities in LLMs to induce undesirable behavior, posing a significant threat to LLM safety. Previous defenses often f

2023

KAPALM: Knowledge grAPh enhAnced Language Models for Fake News Detection

EMNLP 2023long findings

Social media has not only facilitated news consumption, but also led to the wide spread of fake news. Because news articles in social media is usually condensed and full of knowledge entities, existing methods of fake news detection use external entity knowledge. However, majority of these methods f…

Cited by 0SourceScholar
2021

KAN: Knowledge-aware Attention Network for Fake News Detection

AAAI 2021technical

The explosive growth of fake news on social media has drawn great concern both from industrial and academic communities. There has been an increasing demand for fake news detection due to its detrimental effects. Generally, news content is condensed and full of knowledge entities. However, existing…

Cited by 127SourcePDFScholar