Discover the power of social and hidden curriculum to decision making: experiments with enron email and movie newsgroups

Abstract

The power of social values that helps to surreptitiously shape or formulate our behavior patterns is not only inevitable, but also influential as the directions of our decision making can never seem to escape the impact of this hidden agent. Therefore, the search of such power agent can be validated through a machine learning approach that enables us to discover the agent dynamics in which drives the evolution of the social groups in a community. By doing so, we set up the problem by introducing a parameterized probabilistic model for the agent dynamics: the acts of an agent are determined by micro-laws with unknown parameters. Our approach is to identify the appropriate parameters in the model. To solve the problem, we develop heuristic expectation-maximization style algorithms for determining the micro-laws of a community based on either observed communication links between actors, or the observed evolution of social groups. We present the learning results from the synthetic data as well as the findings on real communities, e.g., Enron email and movie newsgroups.