AAlto University, Finland
Aristides Gionis is a professor in the department of Computer Science in Aalto University. His previous appointments include being a visiting professor in the University of Rome and a senior research scientist in Yahoo! Research. He is currently serving as an action editor in the Data Management and Knowledge Discovery journal (DMKD), an associate editor in the ACM Transactions on Knowledge Discovery from Data (TKDD), and an associate editor in the ACM Transactions on the Web (TWEB). He has contributed in several areas of data science, such as algorithmic data analysis, web mining, social-media analysis, data clustering, and privacy-preserving data mining. His current research is funded by the Academy of Finland (projects Nestor, Agra, AIDA) and the European Commission (project SoBigData).
Maximizing diversity in social networks
Online social media are a major venue of public discourse today, hosting the opinions of hundreds of millions of individuals. Social media are often credited for providing a technological means to break information barriers and promote diversity and democracy. In practice, however, the opposite effect is often observed: users tend to favor content that agrees with their existing world-view, get less exposure to conflicting viewpoints, and eventually create "echo chambers" and increased polarization. Arguably, without any kind of moderation, current social-media platforms gravitate towards a state in which net-citizens are constantly reinforcing their existing opinions.
In this talk we present our ongoing line of work on analyzing and moderating online social discussions. We first consider the questions of detecting controversy using network structure and content. We then address the problem of designing algorithms to break filter bubbles, reduce polarization, and increase diversity. We discuss a number of different strategies such as user and content recommendation, as well as approaches based on information cascades.