DescriptionIn this lecture by KAUST Associate Professor of Computer Science Xiangliang Zhang, she will introduce the advantages of the study of social media data with AI models, the unprompted feelings and opinions of users, and the spread of misinformation. Around 4 billion people across the planet (half of the total global population) are using social media, e.g., Facebook, Twitter, Instagram, and LinkedIn, which make available a vast amount of user-generated content, including but not limited to blogs/microblogs, forum discussions, and online reviews of products and services. As an important data source, social media allows us to study users' unprompted feelings and opinions. One fast developing area of social media research is trying to leverage the power of machine learning and AI to analyze user-generated content efficiently. This talk will introduce the advantages of studying social media data with AI models, such as understanding the interests and needs of users for providing better-customed services and identifying sentiment variation during the outbreak of Covid-19 for knowing how people feel in a pandemic. To investigate the downside of social media use, the talk will also discuss the spread of misinformation among connected users causing social media infodemic.
Dr. Xiangliang Zhang is an Associate Professor of Computer Science and directs the MINE (http://mine.kaust.edu.sa) group at KAUST, Saudi Arabia. She earned her Ph.D. degree in computer science from INRIA-University Paris-Sud, France, in July 2010. Dr. Zhang's research mainly focuses on learning from complex and large-scale streaming data and graph data. Dr. Zhang has published over 140 research papers in refereed international journals and conference proceedings, including TKDE, SIGKDD, AAAI, IJCAI, NeurIPS, ICDM, etc. She regularly serves on the Program Committee for premier conferences like SIGKDD (Senior PC), AAAI (Senior PC), IJCAI (Area Chair, Senior PC), etc. Dr. Zhang was invited to deliver an Early Career Spotlight talk at IJCAI-ECAI 2018.
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