7 citations · 12 across the 6 of their papers we have counts for
7 papers
SAFE-MR: Evidence Sufficiency Learning for Selective Multimodal Rumor Detection
Shiwen Ni
Multimodal rumor detectors increasingly rely on retrieved evidence, yet relevant evidence is not necessarily sufficient for verification. Missing provenance, duplicated reports, an…
MVAN: Multi-View Attention Networks for Fake News Detection on Social Media
Shiwen Ni, Jiawen Li, Hung-Yu Kao
Fake news on social media is a widespread and serious problem in today's society. Existing fake news detection methods focus on finding clues from Long text content, such as origin…
ELECTRA is a Zero-Shot Learner, Too
Shiwen Ni, Hung-Yu Kao
Recently, for few-shot or even zero-shot learning, the new paradigm "pre-train, prompt, and predict" has achieved remarkable achievements compared with the "pre-train, fine-tune" p…
True or False: Does the Deep Learning Model Learn to Detect Rumors?
Shiwen Ni, Jiawen Li, Hung-Yu Kao
It is difficult for humans to distinguish the true and false of rumors, but current deep learning models can surpass humans and achieve excellent accuracy on many rumor datasets. I…
HAT4RD: Hierarchical Adversarial Training for Rumor Detection on Social Media
Shiwen Ni, Jiawen Li, Hung-Yu Kao
With the development of social media, social communication has changed. While this facilitates people's communication and access to information, it also provides an ideal platform…
DropAttack: A Masked Weight Adversarial Training Method to Improve Generalization of Neural Networks
Shiwen Ni, Jiawen Li, Hung-Yu Kao
Adversarial training has been proven to be a powerful regularization method to improve the generalization of models. However, current adversarial training methods only attack the o…
Meet The Truth: Leverage Objective Facts and Subjective Views for Interpretable Rumor Detection
Jiawen Li, Shiwen Ni, Hung-Yu Kao
Existing rumor detection strategies typically provide detection labels while ignoring their explanation. Nonetheless, providing pieces of evidence to explain why a suspicious tweet…