Exploring entrainment patterns of human emotion in social media

Saike He, Xiaolong Zheng, Dajun Zeng, Chuan Luo, Zhu Zhang

Research output: Contribution to journalArticle

17 Citations (Scopus)

Abstract

Emotion entrainment, which is generally defined as the synchronous convergence of human emotions, performs many important social functions. However, what the specific mechanisms of emotion entrainment are beyond in-person interactions, and how human emotions evolve under different entrainment patterns in large-scale social communities, are still unknown. In this paper, we aim to examine the massive emotion entrainment patterns and understand the underlying mechanisms in the context of social media. As modeling emotion dynamics on a large scale is often challenging, we elaborate a pragmatic framework to characterize and quantify the entrainment phenomenon. By applying this framework on the datasets from two large-scale social media platforms, we find that the emotions of online users entrain through social networks. We further uncover that online users often form their relations via dual entrainment, while maintain it through single entrainment. Remarkably, the emotions of online users are more convergent in nonreciprocal entrainment. Building on these findings, we develop an entrainment augmented model for emotion prediction. Experimental results suggest that entrainment patterns inform emotion proximity in dyads, and encoding their associations promotes emotion prediction. This work can further help us to understand the underlying dynamic process of large-scale online interactions and make more reasonable decisions regarding emergency situations, epidemic diseases, and political campaigns in cyberspace.

Original languageEnglish (US)
Article numbere0150630
JournalPLoS One
Volume11
Issue number3
DOIs
StatePublished - Mar 1 2016

Fingerprint

Social Media
social networks
emotions
Emotions
prediction
Social Support
Emergencies

ASJC Scopus subject areas

  • Medicine(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Agricultural and Biological Sciences(all)

Cite this

Exploring entrainment patterns of human emotion in social media. / He, Saike; Zheng, Xiaolong; Zeng, Dajun; Luo, Chuan; Zhang, Zhu.

In: PLoS One, Vol. 11, No. 3, e0150630, 01.03.2016.

Research output: Contribution to journalArticle

He, Saike ; Zheng, Xiaolong ; Zeng, Dajun ; Luo, Chuan ; Zhang, Zhu. / Exploring entrainment patterns of human emotion in social media. In: PLoS One. 2016 ; Vol. 11, No. 3.
@article{550def41c2a9461f999adfb8a1b0382e,
title = "Exploring entrainment patterns of human emotion in social media",
abstract = "Emotion entrainment, which is generally defined as the synchronous convergence of human emotions, performs many important social functions. However, what the specific mechanisms of emotion entrainment are beyond in-person interactions, and how human emotions evolve under different entrainment patterns in large-scale social communities, are still unknown. In this paper, we aim to examine the massive emotion entrainment patterns and understand the underlying mechanisms in the context of social media. As modeling emotion dynamics on a large scale is often challenging, we elaborate a pragmatic framework to characterize and quantify the entrainment phenomenon. By applying this framework on the datasets from two large-scale social media platforms, we find that the emotions of online users entrain through social networks. We further uncover that online users often form their relations via dual entrainment, while maintain it through single entrainment. Remarkably, the emotions of online users are more convergent in nonreciprocal entrainment. Building on these findings, we develop an entrainment augmented model for emotion prediction. Experimental results suggest that entrainment patterns inform emotion proximity in dyads, and encoding their associations promotes emotion prediction. This work can further help us to understand the underlying dynamic process of large-scale online interactions and make more reasonable decisions regarding emergency situations, epidemic diseases, and political campaigns in cyberspace.",
author = "Saike He and Xiaolong Zheng and Dajun Zeng and Chuan Luo and Zhu Zhang",
year = "2016",
month = "3",
day = "1",
doi = "10.1371/journal.pone.0150630",
language = "English (US)",
volume = "11",
journal = "PLoS One",
issn = "1932-6203",
publisher = "Public Library of Science",
number = "3",

}

TY - JOUR

T1 - Exploring entrainment patterns of human emotion in social media

AU - He, Saike

AU - Zheng, Xiaolong

AU - Zeng, Dajun

AU - Luo, Chuan

AU - Zhang, Zhu

PY - 2016/3/1

Y1 - 2016/3/1

N2 - Emotion entrainment, which is generally defined as the synchronous convergence of human emotions, performs many important social functions. However, what the specific mechanisms of emotion entrainment are beyond in-person interactions, and how human emotions evolve under different entrainment patterns in large-scale social communities, are still unknown. In this paper, we aim to examine the massive emotion entrainment patterns and understand the underlying mechanisms in the context of social media. As modeling emotion dynamics on a large scale is often challenging, we elaborate a pragmatic framework to characterize and quantify the entrainment phenomenon. By applying this framework on the datasets from two large-scale social media platforms, we find that the emotions of online users entrain through social networks. We further uncover that online users often form their relations via dual entrainment, while maintain it through single entrainment. Remarkably, the emotions of online users are more convergent in nonreciprocal entrainment. Building on these findings, we develop an entrainment augmented model for emotion prediction. Experimental results suggest that entrainment patterns inform emotion proximity in dyads, and encoding their associations promotes emotion prediction. This work can further help us to understand the underlying dynamic process of large-scale online interactions and make more reasonable decisions regarding emergency situations, epidemic diseases, and political campaigns in cyberspace.

AB - Emotion entrainment, which is generally defined as the synchronous convergence of human emotions, performs many important social functions. However, what the specific mechanisms of emotion entrainment are beyond in-person interactions, and how human emotions evolve under different entrainment patterns in large-scale social communities, are still unknown. In this paper, we aim to examine the massive emotion entrainment patterns and understand the underlying mechanisms in the context of social media. As modeling emotion dynamics on a large scale is often challenging, we elaborate a pragmatic framework to characterize and quantify the entrainment phenomenon. By applying this framework on the datasets from two large-scale social media platforms, we find that the emotions of online users entrain through social networks. We further uncover that online users often form their relations via dual entrainment, while maintain it through single entrainment. Remarkably, the emotions of online users are more convergent in nonreciprocal entrainment. Building on these findings, we develop an entrainment augmented model for emotion prediction. Experimental results suggest that entrainment patterns inform emotion proximity in dyads, and encoding their associations promotes emotion prediction. This work can further help us to understand the underlying dynamic process of large-scale online interactions and make more reasonable decisions regarding emergency situations, epidemic diseases, and political campaigns in cyberspace.

UR - http://www.scopus.com/inward/record.url?scp=84961183386&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84961183386&partnerID=8YFLogxK

U2 - 10.1371/journal.pone.0150630

DO - 10.1371/journal.pone.0150630

M3 - Article

C2 - 26953692

AN - SCOPUS:84961183386

VL - 11

JO - PLoS One

JF - PLoS One

SN - 1932-6203

IS - 3

M1 - e0150630

ER -