Quantitative study of individual emotional states in social networks

Jie Tang, Yuan Zhang, Jimeng Sun, Jinhai Rao, Wenjing Yu, Yiran Chen, A. C.M. Fong

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish (US)
Article number5959157
Pages (from-to)132-144
Number of pages13
JournalIEEE Transactions on Affective Computing
Issue number2
StatePublished - Jul 25 2012
Externally publishedYes


  • emotion dynamics
  • predictive model
  • social influence
  • Social network

ASJC Scopus subject areas

  • Software
  • Human-Computer Interaction


Dive into the research topics of 'Quantitative study of individual emotional states in social networks'. Together they form a unique fingerprint.

Cite this