Yuanyuan Lei

Bio: Yuanyuan Lei is a PhD student in the Department of Computer Science at Texas A&M University. Her research direction is Natural Language Processing, Machine Learning, and Deep Learning. More specifically, she works on opinion and argument mining, misinformation detection, as well as their applications into media bias and polarization. At the core of her research on media bias, she aims to build responsible natural language processing models to understand subjective bias like opinions, detect media framing bias, and identify misinformation or disinformation.

Talk Title: Sentence-level Media Bias Analysis through Content Structures Modeling

Abstract: News media plays a vast role not only by providing information, but also by selecting, packaging, and organizing the information to shape public opinions. Media outlets are becoming more partisan and polarized nowadays, and developing sophisticated models to detect media bias becomes necessary. Most prior work in the NLP community focused on detecting media bias either at the source level or the article level. However, articles consist of multiple sentences, and each sentence serves different purposes in narrating a news story. We aim to identify media bias at a more fine-grained sentence level, and pinpoint bias sentences that seek to implant ideological bias and manipulate public opinions. Sentence-level media bias can be very subtle and tends to be presented in a neutral and factual tone. Our research demonstrates that modeling the content structures of news articles can reveal such implicit ideological bias.