Quantitative Study of Individual Emotional States in Social Networks

TLDR

Emotion drives consumer behavior, acting 3,000 times faster than rational thought and eliciting positive or negative responses and physical expressions, making marketing strategies without emotion ineffective. The study aims to quantitatively analyze how an individual's emotional state can be inferred from their historic emotion log and how it interacts with friends in a social network, to help companies tailor marketing and after‑sale services. We develop MoodCast, a quantitative model that infers individual emotional states from historic emotion logs and models their influence among friends, validated on mobile‑based and online virtual networks. Statistical analysis revealed several significant patterns in individual emotional dynamics.

Abstract

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.