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

doi:10.1109/T-AFFC.2011.23

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 journal › Article › peer-review

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.

Original language	English (US)
Article number	5959157
Pages (from-to)	132-144
Number of pages	13
Journal	IEEE Transactions on Affective Computing
Volume	3
Issue number	2
DOIs	https://doi.org/10.1109/T-AFFC.2011.23
State	Published - Jul 25 2012
Externally published	Yes

Keywords

emotion dynamics
predictive model
social influence
Social network

ASJC Scopus subject areas

Software
Human-Computer Interaction

Online availability

10.1109/T-AFFC.2011.23

Library availability

Discover UIUC Full Text

Cite this

@article{1b476442c0b44130871d27e459fc0d5c,

title = "Quantitative study of individual emotional states in social networks",

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.",

keywords = "emotion dynamics, predictive model, social influence, Social network",

author = "Jie Tang and Yuan Zhang and Jimeng Sun and Jinhai Rao and Wenjing Yu and Yiran Chen and Fong, {A. C.M.}",

note = "Funding Information: The work is supported by the Natural Science Foundation of China (No. 61073073 , No. 60973102), Chinese National Key Foundation Research (No. 60933013, No. 61035004), and National High-tech R&D Program (No. 2009AA01Z138). It is also supported by a research award from the Nokia China Research Center.",

year = "2012",

month = jul,

day = "25",

doi = "10.1109/T-AFFC.2011.23",

language = "English (US)",

volume = "3",

pages = "132--144",

journal = "IEEE Transactions on Affective Computing",

issn = "1949-3045",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

number = "2",

}

TY - JOUR

T1 - Quantitative study of individual emotional states in social networks

AU - Tang, Jie

AU - Zhang, Yuan

AU - Sun, Jimeng

AU - Rao, Jinhai

AU - Yu, Wenjing

AU - Chen, Yiran

AU - Fong, A. C.M.

N1 - Funding Information: The work is supported by the Natural Science Foundation of China (No. 61073073 , No. 60973102), Chinese National Key Foundation Research (No. 60933013, No. 61035004), and National High-tech R&D Program (No. 2009AA01Z138). It is also supported by a research award from the Nokia China Research Center.

PY - 2012/7/25

Y1 - 2012/7/25

N2 - 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.

AB - 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.

KW - emotion dynamics

KW - predictive model

KW - social influence

KW - Social network

UR - http://www.scopus.com/inward/record.url?scp=84864074660&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84864074660&partnerID=8YFLogxK

U2 - 10.1109/T-AFFC.2011.23

DO - 10.1109/T-AFFC.2011.23

M3 - Article

AN - SCOPUS:84864074660

SN - 1949-3045

VL - 3

SP - 132

EP - 144

JO - IEEE Transactions on Affective Computing

JF - IEEE Transactions on Affective Computing

IS - 2

M1 - 5959157

ER -

Quantitative study of individual emotional states in social networks

Abstract

Keywords

ASJC Scopus subject areas

Online availability

Library availability

Related links

Fingerprint

Cite this