61 citations · 116 across the 5 of their papers we have counts for
5 papers
A Large-Scale Comparative Study of Accurate COVID-19 Information versus Misinformation
Yida Mu, Ye Jiang, Freddy Heppell +4
The COVID-19 pandemic led to an infodemic where an overwhelming amount of COVID-19 related content was being disseminated at high velocity through social media. This made it challe…
Team QUST at SemEval-2023 Task 3: A Comprehensive Study of Monolingual and Multilingual Approaches for Detecting Online News Genre, Framing and Persuasion Techniques
Ye Jiang
This paper describes the participation of team QUST in the SemEval2023 task 3. The monolingual models are first evaluated with the under-sampling of the majority classes in the ear…
Similarity-Aware Multimodal Prompt Learning for Fake News Detection
Ye Jiang, Xiaomin Yu, Yimin Wang +3
The standard paradigm for fake news detection mainly utilizes text information to model the truthfulness of news. However, the discourse of online fake news is typically subtle and…
Categorising Fine-to-Coarse Grained Misinformation: An Empirical Study of COVID-19 Infodemic
Ye Jiang, Xingyi Song, Carolina Scarton +2
The spreading COVID-19 misinformation over social media already draws the attention of many researchers. According to Google Scholar, about 26000 COVID-19 related misinformation st…
Classification Aware Neural Topic Model and its Application on a New COVID-19 Disinformation Corpus
Xingyi Song, Johann Petrak, Ye Jiang +3
The explosion of disinformation accompanying the COVID-19 pandemic has overloaded fact-checkers and media worldwide, and brought a new major challenge to government responses world…