Xia Zeng

Xia Zeng is a Researcher affiliated with the School of Electronic Engineering and Computer Science at Queen Mary University of London. Her work focuses on advancing automated fact-checking systems through innovative applications of natural language processing (NLP) and artificial intelligence. She explores interdisciplinary approaches that integrate large language models, crowdsourcing techniques, and semantic analysis to combat misinformation.

Her research interests include misinformation detection, few-shot learning methodologies, and the ethical implications of AI in journalism. Notable contributions include developing frameworks like MAPLE for analyzing pairwise language evolution and Active PETs for optimizing data annotation in claim verification tasks. Zeng's publications highlight a strong emphasis on practical applications of NLP to real-world challenges such as scientific claim validation and social media content analysis.

Zeng's research trends reveal a consistent focus on improving the accuracy and efficiency of automated fact-checking through hybrid human-AI systems. Her work bridges computational techniques with linguistic analysis, addressing both technical and societal dimensions of information verification. No scientific awards are explicitly mentioned in available records, though her prolific publication output indicates significant scholarly engagement.

While no advising or grant information is provided here, her involvement with Queen Mary's School of Electronic Engineering and Computer Science suggests potential collaborations with academic and industry partners. She is part of a research community exploring cutting-edge AI solutions for information integrity challenges.