A Study of Information Diffusion over a Realistic Social Network Model
Sociological models of human behavior can explain population-level phenomena within social systems; computer modeling can simulate a wide variety of scenarios and allow one to pose and test hypotheses about the social system. In this paper, we model and examine the spread of information through personal conversations in a simulated socio-technical network that provides a high degree of realism and a great deal of captured detail. To our knowledge thisis the first time information spread via conversation has been modeled against a statistically accurate simulation of people's daily interactions within a specific urban or rural environment, capturing the points in time and space at which two people could converse, and providing a realistic basis formodeling human behavior during face-to-face interaction. We use a probabilistic model to decide whether two people will converse about a particular topic based on their similarity and familiarity. Similarity is modeled by matching selected demographic characteristics, while familiarity is modeled by the amount of contact required to convey information. We report our findings on the effects of familiarity and similarity on the spread of information over the social network. We resolve the results by age group, daily activities, time, household income, household size and examine the relative effect of these factors.For informal topics where little familiarity is required, shopping and recreational activities predominate; otherwise, home, work, and school predominate. We find that youths play a significant role in spreading information through a community rapidly, mainly through interactions in schools and recreational activities.