14 citations · 43 across the 14 of their papers we have counts for
14 papers
A Dataset for Analysing News Framing in Chinese Media
Owen Cook, Yida Mu, Xinye Yang +2
Framing is an essential device in news reporting, allowing the writer to influence public perceptions of current affairs. While there are existing automatic news framing detection…
Enhancing Data Quality through Simple De-duplication: Navigating Responsible Computational Social Science Research
Yida Mu, Mali Jin, Xingyi Song +1
Research in natural language processing (NLP) for Computational Social Science (CSS) heavily relies on data from social media platforms. This data plays a crucial role in the devel…
Addressing Topic Granularity and Hallucination in Large Language Models for Topic Modelling
Yida Mu, Peizhen Bai, Kalina Bontcheva +1
Large language models (LLMs) with their strong zero-shot topic extraction capabilities offer an alternative to probabilistic topic modelling and closed-set topic classification app…
Large Language Models Offer an Alternative to the Traditional Approach of Topic Modelling
Yida Mu, Chun Dong, Kalina Bontcheva +1
Topic modelling, as a well-established unsupervised technique, has found extensive use in automatically detecting significant topics within a corpus of documents. However, classic…
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…
Examining the Limitations of Computational Rumor Detection Models Trained on Static Datasets
Yida Mu, Xingyi Song, Kalina Bontcheva +1
A crucial aspect of a rumor detection model is its ability to generalize, particularly its ability to detect emerging, previously unknown rumors. Past research has indicated that c…
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…
Navigating Prompt Complexity for Zero-Shot Classification: A Study of Large Language Models in Computational Social Science
Yida Mu, Ben P. Wu, William Thorne +5
Instruction-tuned Large Language Models (LLMs) have exhibited impressive language understanding and the capacity to generate responses that follow specific prompts. However, due to…
Examining Temporalities on Stance Detection towards COVID-19 Vaccination
Yida Mu, Mali Jin, Kalina Bontcheva +1
Previous studies have highlighted the importance of vaccination as an effective strategy to control the transmission of the COVID-19 virus. It is crucial for policymakers to have a…
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…
VaxxHesitancy: A Dataset for Studying Hesitancy towards COVID-19 Vaccination on Twitter
Yida Mu, Mali Jin, Charlie Grimshaw +3
Vaccine hesitancy has been a common concern, probably since vaccines were created and, with the popularisation of social media, people started to express their concerns about vacci…
Identifying and Characterizing Active Citizens who Refute Misinformation in Social Media
Yida Mu, Pu Niu, Nikolaos Aletras
The phenomenon of misinformation spreading in social media has developed a new form of active citizens who focus on tackling the problem by refuting posts that might contain misinf…