Natural Language Processing for Assessing Health News Quality （大数据时代情报学与情报工作系列讲座）
Bei Yu is an Associate Professor at the School of Information Studies at Syracuse University. Her research area is in applied natural language processing, especially sentiment and opinion analysis.
Before joining Syracuse she was a postdoctoral researcher at the Kellogg School of Management at Northwestern University. Dr. Yu earned her PhD from the Graduate School of Library and Information Science at University of Illinois at Urbana-Champaign. She holds both BS and MS degrees in computer science.
Her primary research interest is in the area of applied Natural Language Processing (applied NLP). Her research focuses on using machine learning, data mining, and language technologies to study long-standing questions in social sciences, humanities, and library and information science.
Specifically, She conducts computational analysis of large amounts of text data to discover linguistic patterns that characterize people's opinions, sentiments, and identities, and develop prediction models accordingly.
Her current work, funded by an IMLS Early Career Award, focuses on developing an automated citation context analysis tool to help researchers retrieve and summarize academic opinions in literature.
Inaccurate and even fake
health news are prevalent on the Internet. They spread exaggerated and fabricated claims that can
harm public health. Assessing the quality of health news has been a challenge
for health consumers. Verifying the claims from various health news sources
requires high level of health and ehealth literacy. This talk introduces our
preliminary work on using natural language processing techniques to assess
health news quality, and thus to assist consumers in health information