Abstract

Twitter is a major social media platform in which users send and read messages ("tweets") of up to 140 characters. In recent years this communication medium has been used by those affected by crises to organize demonstrations or find relief. Because traffic on this media platform is extremely heavy, with hundreds of millions of tweets sent every day, it is difficult to differentiate between times of turmoil and times of typical discussion. In this work we present a new approach to addressing this problem. We first assess several possible "thermostats" of activity on social media for their effectiveness in finding important time periods. We compare methods commonly found in the literature with a method from economics. By combining methods from computational social science with methods from economics, we introduce an approach that can effectively locate crisis events in the mountains of data generated on Twitter. We demonstrate the strength of this method by using it to locate the social events relating to the Occupy Wall Street movement protests at the end of 2011.

Original languageEnglish (US)
Article numbere102001
JournalPLoS One
Volume9
Issue number7
DOIs
StatePublished - Jul 30 2014

Fingerprint

Thermostats
Economics
Social sciences
Social Media
social networks
Demonstrations
thermostats
Communications Media
Communication
mass media
economics
methodology
social sciences
Social Sciences
traffic
mountains

ASJC Scopus subject areas

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

Cite this

Discovering social events through online attention. / Kenett, Dror Y.; Morstatter, Fred; Stanley, H. Eugene; Liu, Huan.

In: PLoS One, Vol. 9, No. 7, e102001, 30.07.2014.

Research output: Contribution to journalArticle

Kenett, DY, Morstatter, F, Stanley, HE & Liu, H 2014, 'Discovering social events through online attention', PLoS One, vol. 9, no. 7, e102001. https://doi.org/10.1371/journal.pone.0102001
Kenett, Dror Y. ; Morstatter, Fred ; Stanley, H. Eugene ; Liu, Huan. / Discovering social events through online attention. In: PLoS One. 2014 ; Vol. 9, No. 7.
@article{a8b190b2383a468d89591d49d40390ec,
title = "Discovering social events through online attention",
abstract = "Twitter is a major social media platform in which users send and read messages ({"}tweets{"}) of up to 140 characters. In recent years this communication medium has been used by those affected by crises to organize demonstrations or find relief. Because traffic on this media platform is extremely heavy, with hundreds of millions of tweets sent every day, it is difficult to differentiate between times of turmoil and times of typical discussion. In this work we present a new approach to addressing this problem. We first assess several possible {"}thermostats{"} of activity on social media for their effectiveness in finding important time periods. We compare methods commonly found in the literature with a method from economics. By combining methods from computational social science with methods from economics, we introduce an approach that can effectively locate crisis events in the mountains of data generated on Twitter. We demonstrate the strength of this method by using it to locate the social events relating to the Occupy Wall Street movement protests at the end of 2011.",
author = "Kenett, {Dror Y.} and Fred Morstatter and Stanley, {H. Eugene} and Huan Liu",
year = "2014",
month = "7",
day = "30",
doi = "10.1371/journal.pone.0102001",
language = "English (US)",
volume = "9",
journal = "PLoS One",
issn = "1932-6203",
publisher = "Public Library of Science",
number = "7",

}

TY - JOUR

T1 - Discovering social events through online attention

AU - Kenett, Dror Y.

AU - Morstatter, Fred

AU - Stanley, H. Eugene

AU - Liu, Huan

PY - 2014/7/30

Y1 - 2014/7/30

N2 - Twitter is a major social media platform in which users send and read messages ("tweets") of up to 140 characters. In recent years this communication medium has been used by those affected by crises to organize demonstrations or find relief. Because traffic on this media platform is extremely heavy, with hundreds of millions of tweets sent every day, it is difficult to differentiate between times of turmoil and times of typical discussion. In this work we present a new approach to addressing this problem. We first assess several possible "thermostats" of activity on social media for their effectiveness in finding important time periods. We compare methods commonly found in the literature with a method from economics. By combining methods from computational social science with methods from economics, we introduce an approach that can effectively locate crisis events in the mountains of data generated on Twitter. We demonstrate the strength of this method by using it to locate the social events relating to the Occupy Wall Street movement protests at the end of 2011.

AB - Twitter is a major social media platform in which users send and read messages ("tweets") of up to 140 characters. In recent years this communication medium has been used by those affected by crises to organize demonstrations or find relief. Because traffic on this media platform is extremely heavy, with hundreds of millions of tweets sent every day, it is difficult to differentiate between times of turmoil and times of typical discussion. In this work we present a new approach to addressing this problem. We first assess several possible "thermostats" of activity on social media for their effectiveness in finding important time periods. We compare methods commonly found in the literature with a method from economics. By combining methods from computational social science with methods from economics, we introduce an approach that can effectively locate crisis events in the mountains of data generated on Twitter. We demonstrate the strength of this method by using it to locate the social events relating to the Occupy Wall Street movement protests at the end of 2011.

UR - http://www.scopus.com/inward/record.url?scp=84904968382&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84904968382&partnerID=8YFLogxK

U2 - 10.1371/journal.pone.0102001

DO - 10.1371/journal.pone.0102001

M3 - Article

VL - 9

JO - PLoS One

JF - PLoS One

SN - 1932-6203

IS - 7

M1 - e102001

ER -