He is an Assistant Professor of Electrical Engineering and Computer Science at Syracuse University. His research interests are in Data Mining, Machine Learning, Social Media Mining, and Social Network Analysis.
His research has been published in major academic venues and highlighted in various scientific and news outlets. He is the principal author of “Social Media Mining: An Introduction” a textbook by Cambridge University Press and the associate editor for SIGKDD Explorations and Frontiers in communication. He is the winner of the NSF CAREER award, President’s Award for Innovation, and outstanding teaching award at Arizona State University.
Fake news detection: Limited Ground Truth, Limited Text, No Understanding of Spreading Intent
“Fake news” is now viewed as one of the greatest threats to democracies and journalism. The massive spread of fake news has weakened public trust in governments and its potential impact on various political outcomes such as the Brexit is yet to be realized. We will briefly review fake news detection techniques, along with some of the current challenges that these methods face. We will discuss some recent advancements to tackle these challenges, particularly focusing on multi-modal fake news analysis and assessing the intent of fake news spreaders.