activity
20182022
most citedMDFEND: Multi-domain Fake News Detection

240 citations · 255 across the 5 of their papers we have counts for

collaborators

7 papers

cs.CL20224 cited

A Prompting-based Approach for Adversarial Example Generation and Robustness Enhancement

Yuting Yang, Pei Huang, Juan Cao +5

Recent years have seen the wide application of NLP models in crucial areas such as finance, medical treatment, and news media, raising concerns of the model robustness and vulnerab…

cs.CV2022

DRAG: Dynamic Region-Aware GCN for Privacy-Leaking Image Detection

Guang Yang, Juan Cao, Qiang Sheng +3

The daily practice of sharing images on social media raises a severe issue about privacy leakage. To address the issue, privacy-leaking image detection is studied recently, with th…

cs.CL20221 cited

Quantifying Robustness to Adversarial Word Substitutions

Yuting Yang, Pei Huang, FeiFei Ma +4

Deep-learning-based NLP models are found to be vulnerable to word substitution perturbations. Before they are widely adopted, the fundamental issues of robustness need to be addres…

cs.CL2022240 cited

MDFEND: Multi-domain Fake News Detection

Qiong Nan, Juan Cao, Yongchun Zhu +2

Fake news spread widely on social media in various domains, which lead to real-world threats in many aspects like politics, disasters, and finance. Most existing approaches focus o…

cs.MM2019

Exploiting Multi-domain Visual Information for Fake News Detection

Peng Qi, Juan Cao, Tianyun Yang +2

The increasing popularity of social media promotes the proliferation of fake news. With the development of multimedia technology, fake news attempts to utilize multimedia contents…

cs.CL2019

How to Write High-quality News on Social Network? Predicting News Quality by Mining Writing Style

Yuting Yang, Juan Cao, Mingyan Lu +2

Rapid development of Internet technologies promotes traditional newspapers to report news on social networks. However, people on social networks may have different needs which natu…