Mining Reliable Information from Crowdsourced Data
(Pictured) Jing Gao, Associate Professor in the Department of Computer Science and Engineering at the University at Buffalo (UB), State University of New York
Jing Gao, Computer Science Assistant Professor at SUNY Buffalo, will present her research on information veracity challenge in crowdsourced data: this is, the problem of determining whether information that is gathered by crowdsourcing is reliable or not. She will be speaking at 10am this Thursday, April 11, in Rice Hall 242. More information on her talk, titled "Mining Reliable Information from Crowdsourced Data," is below. Questions? Contact Yangfeng Ji (firstname.lastname@example.org).
Speaker: Jing Gao
Date: Thursday, April 11th 2019
Time: 10:00 a.m. – 11:00 a.m.
Location: Rice Hall, Room 242
Host: Yangfeng Ji (yj3fs)
Title: Mining Reliable Information from Crowdsourced Data
Abstract: With the proliferation of mobile devices and social media platforms, any person can publicize observations about any activity, event or object anywhere and at any time. The confluence of these enormous crowdsourced data can contribute to an inexpensive, sustainable and large-scale decision support system that has never been possible before. The main obstacle in building such a system lies in the problem of information veracity, i.e., it is hard to distinguish true or accurate information from false or inaccurate ones. In this talk, I will present our efforts towards solving information veracity challenge when crowdsourced data are ubiquitous but their reliability is suspect. We model the task as an optimization problem that jointly searches for source reliability and true facts without any supervision. We showed how our proposed models handle different kinds of data, including data with long-tail distributions, data of heterogeneous types, spatial-temporal data, streaming and distributed data, and how they can support a wide range of applications, including crowdsourcing question answering, knowledge base construction and environmental monitoring. To motivate crowd users to contribute high-quality data, we designed effective budget allocation and privacy preservation mechanisms. At the end of the talk, I will briefly introduce my other work, which is the integration of complementary views for improved inference for fake news detection on social media data as well as several decision making tasks in healthcare and transportation domains.
About the speaker: Jing Gao is an Associate Professor in the Department of Computer Science and Engineering at the University at Buffalo (UB), State University of New York. She received her PhD from Computer Science Department, University of Illinois at Urbana Champaign in 2011, and subsequently joined UB in 2012. She is broadly interested in data and information analysis with a focus on information integration, crowdsourcing, social media analysis, misinformation detection, knowledge graphs, multi-source data analysis, anomaly detection, transfer learning and data stream mining. She enjoyed collaborating with researchers across multiple disciplines, including healthcare, transportation and cyber security. She has published more than 150 papers in referred journals and conferences. She is an editor of ACM Transactions on Intelligence Systems and Technology, and serves in the senior program committee of ACM KDD and CIKM conferences. She is a recipient of NSF CAREER award and IBM faculty award