Xingyi Song

Dr. Xingyi Song serves as a Lecturer in Computational Media Analysis at the Department of Computer Science, University of Sheffield, where he is an active member of the Natural Language Processing research group and the GATE team. His academic career began with MSc and PhD studies at the same institution, followed by professional roles as a machine translation specialist at Iconic Translation Machine (2015-2016) and Research Associate for EU-funded projects including Kconnect, Knowmak, and Risis2 (2016-2021).

His research spans multiple critical domains in computational linguistics and social impact applications. Primary interests include Natural Language Processing with specialized focus on sentiment analysis, computational social science methodologies, and bio-medical text processing. His work demonstrates strong interdisciplinary connections between AI techniques and real-world challenges in public health communication, misinformation detection, and social media analysis.

Analysis of his recent publications reveals a clear trajectory toward multimodal and large language model applications for social good. Key trends include fake news detection using cross-modal approaches, temporal analysis of vaccine hesitancy narratives during the pandemic, and innovative applications of LLMs for topic modeling and data quality enhancement. His work consistently bridges theoretical NLP advancements with practical societal applications.

Dr. Song currently leads the ASIMOV project (Innovate UK, 2024-2025, £142,691) as Principal Investigator and serves as Co-Principal Investigator for the vera.ai project (Horizon Europe, 2022-2025, £776,703), demonstrating significant research leadership in AI verification systems. His collaborative network extends across multiple EU projects and interdisciplinary teams focused on misinformation detection and social media analysis.

As part of the Natural Language Processing research group and GATE team, Dr. Song contributes to Sheffield's internationally recognized expertise in language technologies. His work integrates closely with healthcare applications through collaborations with medical researchers and NHS partners, particularly in mental health text analysis and vaccine communication studies.