An Agent-Based Modeling Framework for Online Collective Emotions

Abstract

Online communication takes a variety of shapes in the different technological media that allow users interact with each other, with their friends, or with arbitrarily large groups. These serve as breeding grounds for collective emotions, in which large amounts of users share emotional states through time. We present our modeling framework for collective emotions in online communities, which can be adapted for the different kinds of online interaction present in the cyberspace. This framework allows the design of agent-based models, in which agents’ emotional states are represented according to psychological theories. This approach aims at a unification of modeling efforts, connecting the sentiment analysis of big data with psychological experiments, through tractable agent-based models. We illustrate the applications of this framework to different online communities, including product reviews, chatrooms, virtual realities, and social networking sites. We show how our model reproduces properties of collective emotions in the reviews of Amazon, and the group discussions of IRC channels. We comment the applications of this framework for data-driven simulation of emotions, and how we formulate testable hypotheses of emotion dynamics for future research on the field.