Quantifying and Reducing Polarization on Social media

Social media has brought a revolution on how people get exposed to information and how they are consuming news. Beyond the undoubtedly large number of advantages and capabilities brought by social-media platforms, a point of criticism has been the creation of filter bubbles or echo chambers, caused by social homophily as well as by algorithmic personalisation and recommendation in content delivery. In this talk, I will present the methods we developed to (i) detect and quantify the existence of polarization on social media, (ii) monitor the evolution of polarisation over time, and finally, (iii) devise methods to overcome the effects caused by increased polarization. We build on top of existing studies and ideas from social science with principles from graph theory to design algorithms which are language independent, domain agnostic and scalable to large number of users.

Kiran Garimella is a PhD student at Aalto University. His research focuses on identifying and combating polarization on social media. In general he is interested in making use of large public datasets to understand human behaviour. Prior to starting his PhD, he worked as a Research Engineer at Yahoo Research, QCRI and as an intern at Carnegie Mellon University, LinkedIn and Amazon. His work on polarization on social media received the best student paper award at WSDM’17 and a best paper nomination at Webscience 2017.