Mining the Social Media

Abstract:
A large part of the Web, today, consists of online platforms that allow their users to generate digital content. They include online social networks, multimedia-sharing web- sites, blogging platforms, and online discussion boards, to name a few examples. Users of those platforms generate content in the form of digital items (e.g. documents, images, or videos), inspect content generated by others, and, finally, interact with each other (e.g. by commenting on each other’s generated items). For the social process of information exchange they enable, such platforms are customarily referred to as ‘social media’.

Users choose the discussions they engage in and who they interact with, and their choices and actions reflect what they find important. My past work consists in defining and quantifying notions of importance for items, users, and social connections between users, and, based on those definitions, propose efficient algorithms to detect important instances of social media activity. In this talk, I'm going to give a brief overview of that work and discuss future directions for research.

Bio:
Michael Mathioudakis received his PhD from the University of Toronto in 2013. His research interests include social media analytics, web mining, and text analysis. He is currently a postdoctoral researcher at the Helsinki Institute for Information Technology (HIIT).