Social Evolution: Opinions and Behaviors in Face-to-Face Networks

Exposure to new ideas and opinions, and their diffusion within social networks, are important questions in education, business, and government. However until recently there has been no method to automatically capture fine-grained face-to-face interactions between people, to better model the diffusion process. In this thesis, we describe the use of colocation and communication sensors in ‘socially aware’ mobile phones to model the spread of opinions and behaviors of 78 residents of an undergraduate residence hall for an entire academic year, based on over 320,000 hours of behavior data. Political scientists (Huckfeldt and Sprague, APSR, 1983) have noted the problem of mutual causation between face-to-face networks and political opinions. During the last three months of the 2008 US presidential campaigns of Barack Obama and John McCain, we find that political discussants have characteristic interaction patterns that can be used to recover the self-reported ‘political discussant’ ties within the community. Automatically measured mobile phone features allow us to estimate exposure to different types of opinions in this community. We propose a measure of ‘dynamic homophily’ which reveals surprising short-term, population-wide behavior changes around external political events such as election debates and Election Day. To our knowledge, this is the first time such dynamic homophily effects have been measured. We find that social exposure to peers in the network predicts individual future opinions (R2 ≈ 0.8, p < 0.001). The use of mobile phone based dynamic exposure increases the explained variance for future political opinions by up to 30%. It is well known that face-to-face networks are the main vehicle for airborne contagious diseases (Elliott, Spatial Epidemiology, 2000). However, epidemiologists have not had access to tools to quantitatively measure the likelihood of contagion, as a function of contact/exposure with infected individuals, in realistic scenarios (Musher, NEJM, 2003), since it requires data about both symptoms and social interactions between individuals. We use of co-location and communication sensors to understand the role of face-to-face interactions in the contagion process. We find that there are characteristic changes in behavior when individuals become sick, reflected in features like total communication, temporal structure in communication (e.g., late nights and weekends), interaction diversity, and movement entropy (both