Data science over graphs, streams, and sequences: From the analysis of fake news to prediction and intervention

Fake news and misinformation have been a serious problem for a long time and the advent of social media has made it more acute, particularly in the context of the 2016 U.S. Presidential election. This illustrates how social networks and media have started playing a fundamental role in the lives of most people--they influence the way we receive, assimilate, and share information with others. As such, online lives of users in social media tend to leave behind a trail of data which can be harnessed for driving applications that either did not exist or could not be launched effectively before. This project will develop a multi-prong approach to detect fake news. It will develop text mining techniques to analyze "tweets" and explore the possible use of new statistical tools for the analysis of social network and media data. In particular, Twitter data will be used to learn user intents and specifically analyze the language for discriminate features of fake news by comparing with genuine ones. This project aims to devise strategies to combat and contain fake news propagation, reducing its consequence and harm for the effective functioning of a civil society.