Quantitative Study of Individual Emotional States in Social Networks

Marketing strategies without emotion will not work. Emotion stimulates the mind 3,000 times quicker than rational thought. Such emotion invokes either a positive or a negative response and physical expressions. Understanding the underlying dynamics of users´ emotions can efficiently help companies formulate marketing strategies and support after-sale services. While prior work has focused mainly on qualitative aspects, in this paper we present our research on quantitative analysis of how an individual´s emotional state can be inferred from her historic emotion log and how this person´s emotional state influences (or is influenced by) her friends in the social network. We statistically study the dynamics of individual´s emotions and discover several interesting as well as important patterns. Based on this discovery, we propose an approach referred to as MoodCast to learn to infer individuals´ emotional states. In both mobile-based social network and online virtual network, we verify the effectiveness of our proposed approach.