Humans as well as information are organized in networks. Interacting with these networks is part of our daily lives: we talk to friends in our social network; we find information by navigating the Web; and we form opinions by listening to others and to the media. Thus, understanding, predicting, and enhancing human behavior in networks poses important research problems for computer and data science with practical applications of high impact. In this talk I will present some of my work in this area, focusing on (1) human navigation of information networks and (2) person-to-person opinions in social networks.
Network navigation constitutes a fundamental human behavior: in order to make use of the information and resources around us, we constantly explore, disentangle, and navigate networks such as the Web. Studying navigation patterns lets us understand better how humans reason about complex networks and lets us build more human-friendly information systems. As an example, I will present an algorithm for improving website hyperlink structure by mining raw web server logs. The resulting system is being deployed on Wikipedia's full server logs at terabyte scale, producing links that are clicked 10 times as frequently as the average link added by human Wikipedia editors.
Communication and coordination through natural language is another prominent human network behavior. Studying the interplay of social network structure and language has the potential to benefit both sociolinguistics and natural language processing. Intriguing opportunities and challenges have arisen recently with the advent of online social media, which produce large amounts of both network and natural language data. As an example, I will discuss my work on person-to-person sentiment analysis in social networks, which combines the sociological theory of structural balance with techniques from natural language processing, resulting in a machine learning model for sentiment prediction that clearly outperforms both text-only and network-only versions.
I will conclude the talk by sketching interesting future directions for computational approaches to studying and enhancing human behavior in networks.
Robert West is a sixth-year Ph.D. candidate in Computer Science in the Infolab at Stanford University, advised by Jure Leskovec. His research aims to understand, predict, and enhance human behavior in social and information networks by developing techniques in data science, data mining, network analysis, machine learning, and natural language processing. Previously, he obtained a Master's degree from McGill University in 2010 and a Diplom degree from Technische Universität München in 2007.