4 citations · 8 across the 5 of their papers we have counts for
5 papers
Don't Waste a Single Annotation: Improving Single-Label Classifiers Through Soft Labels
Ben Wu, Yue Li, Yida Mu +3
In this paper, we address the limitations of the common data annotation and training methods for objective single-label classification tasks. Typically, when annotating such tasks…
Examining Temporal Bias in Abusive Language Detection
Mali Jin, Yida Mu, Diana Maynard +1
The use of abusive language online has become an increasingly pervasive problem that damages both individuals and society, with effects ranging from psychological harm right throug…
Classification-Aware Neural Topic Model Combined With Interpretable Analysis -- For Conflict Classification
Tianyu Liang, Yida Mu, Soonho Kim +4
A large number of conflict events are affecting the world all the time. In order to analyse such conflict events effectively, this paper presents a Classification-Aware Neural Topi…
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…
It's about Time: Rethinking Evaluation on Rumor Detection Benchmarks using Chronological Splits
Yida Mu, Kalina Bontcheva, Nikolaos Aletras
New events emerge over time influencing the topics of rumors in social media. Current rumor detection benchmarks use random splits as training, development and test sets which typi…