![]() ![]() Gold Medal in Chinese National Chemistry Olympiad (1992) Outstanding Student Scholarship, Peking University (1995&1994) Peking University Student Research Scholarship (1996)Įxcellent Student in Honors Science Program (1996) MIT Technology Review TR35 Award (Top 35 Young Innovators under the age of 35) (2006)Ĭornell University Graduate School Travel Grants (2001) Research Corporation Cottrell Scholar Award (2007) Research Corporation Scialog Award for Solar Energy Conversion (2011)ĪCS ExxonMobil Solid State Chemistry Fellowship Award (2008) Research Corporation SciaLog Collaborative Innovation Award for Solar Energy Conversion (2012) in Chemistry, Peking (Beijing) University, Beijing, China. LieberĢ002 PhD in Chemistry, Cornell University, Ithaca, NY. Phone: (608)262-1562, FAX: (608)262-0453, E-mail: ĩ/13 to present Professor, Department of Chemistry, University of Wisconsin-Madisonħ/10 to 8/13 Associate Professor, Department of Chemistry, University of Wisconsin-MadisonĨ/04 to 6/10 Assistant Professor, Department of Chemistry, University of Wisconsin-Madisonġ/05 to present Faculty member of Materials Science Program (MSP), UW-MadisonĢ002-2004 Postdoctoral FellowHarvard University, Cambridge, MA. Extensive experiments on this dataset show that the proposed WeFEND model achieves the best performance compared with the state-of-the-art methods.Professor, Department of Chemistry, University of Wisconsin-Madisonġ101 University Avenue, Madison, Wisconsin 53706 We tested the proposed framework on a large collection of news articles published via WeChat official accounts and associated user reports. The fake news detector aims to identify fake news based on the news content. The reinforced selector using reinforcement learning techniques chooses high-quality samples from the weakly labeled data and filters out those low-quality ones that may degrade the detector's prediction performance. The annotator can automatically assign weak labels for unlabeled news based on users' reports. The proposed framework consists of three main components: the annotator, the reinforced selector and the fake news detector. In order to tackle this challenge, we propose a reinforced weakly-supervised fake news detection framework, i.e., WeFEND, which can leverage users' reports as weak supervision to enlarge the amount of training data for fake news detection. Therefore, how to obtain fresh and high-quality labeled samples is the major challenge in employing deep learning models for fake news detection. Moreover, due to the dynamic nature of news, annotated samples may become outdated quickly and cannot represent the news articles on newly emerged events. ![]() However, the training of such models requires a large amount of labeled data, but manual annotation is time-consuming and expensive. Recently, deep learning based approaches have shown improved performance in fake news detection. Therefore, it is extremely important to detect fake news as early as possible. ![]() Via social media platforms, fake news travel at unprecedented speeds, reach global audiences and put users and communities at great risk. Today social media has become the primary source for news. ![]()
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