Collaborative Research: SoCS: Analysis of Social Media Driven By Theories of Political Psychology

Carnegie Mellon University, Pittsburgh PA

Investigators

Abstract

Understanding how people make decisions in complex settings is crucial in many application areas, including marketing, intelligence analysis, and political decision making. Traditionally, human decision-makers are modeled as rational agents seeking to maximize some mathematical measure of utility. In fact, however, people are overwhelmed in information-rich environments, and have developed cognitive and emotional strategies to navigate such environments. One such strategy is "motivated reasoning", where information is first evaluated subconsciously for emotional content, with the goal of maintaining an existing emotional commitment, and cognitive processing of the information is then conditioned on this emotional evaluation. Political scientists have demonstrated motivated reasoning in evaluation of both candidates and issues. In general, decision-making and information-gathering are strongly influenced by emotion, prior knowledge, and the social communities to which a person belongs. Evidence suggests that accurate models of human decision-making must be complex enough to model not only utility, but prior knowledge and beliefs, human cognitive abilities, and social context. Building such cognitive models requires substantially extending the state-of-the-art in machine learning. In the past, the ability of researchers in political psychology to develop such complex models of decision-making was limited by the amount of data obtainable obtain from surveys or human-subject experiments. The recent explosion of on-line political communities provides an opportunity to overcome this limitation. We will model human behavior for socially-driven information gathering and decision-making tasks - specifically for political decisions - by combining human-subject experiments with analysis of large datasets of social media and social interactions.