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Use trend analysis of twitter after the great east japan earthquake

Authors:

Mai Miyabe,

Asako Miura,

Eiji AramakiAuthors Info & Claims

CSCW '12: Proceedings of the ACM 2012 conference on Computer Supported Cooperative Work Companion

Pages 175 - 178

https://doi.org/10.1145/2141512.2141571

Published: 11 February 2012 Publication History

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Abstract

After the Great East Japan Earthquake in Japan 2011, numerous tweets were exchanged on Twitter. Several studies have already pointed out that micro-blogging systems have shown potential advantages in emergency situations, but it remains unclear how people use them. This paper presents a case study of how people used Twitter after the Great East Japan Earthquake. First, we gathered tweets immediately after the earthquake and analyzed various factors, including locations. The results revealed two findings: (1) people in the disaster area tend to directly communicate with each other (reply-based tweet). On the other hand,(2) people in the other area prefer spread the information from the disaster area by using Re-tweet.

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K. Starbird, L. Palen, A. L. Hughes, and S. Vieweg. Chatter on The Red: What Hazards Threat Reveals about the Social Life of Microblogged Information. In Proc. Computer-Supported Cooperative Work (CSCW), 2010.

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S. Vieweg, A. L. Hughes, K. Starbird, and L. Palen. Microblogging during two natural hazards events: what Twitter may contribute to situational awareness. In Proc. Conf. on Human Factors in Computing Systems (CHI), pp. 1079--1088, 2010.

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Ma YZhou QTag BSarsenbayeva ZKnibbe JGoncalves J(2023)“Hello, Fellow Villager!”: Perceptions and Impact of Displaying Users’ Locations on WeiboHuman-Computer Interaction – INTERACT 202310.1007/978-3-031-42286-7_29(511-532)Online publication date: 25-Aug-2023
https://doi.org/10.1007/978-3-031-42286-7_29
Contreras DWilkinson SAktas YFallou LBossu RLandès M(2022)Intensity-Based Sentiment and Topic Analysis. The Case of the 2020 Aegean EarthquakeFrontiers in Built Environment10.3389/fbuil.2022.8397708Online publication date: 1-Mar-2022
https://doi.org/10.3389/fbuil.2022.839770
Contreras DWilkinson SAlterman EHervás J(2022)Accuracy of a pre-trained sentiment analysis (SA) classification model on tweets related to emergency response and early recovery assessment: the case of 2019 Albanian earthquakeNatural Hazards10.1007/s11069-022-05307-w113:1(403-421)Online publication date: 23-Mar-2022
https://doi.org/10.1007/s11069-022-05307-w
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Index Terms

Use trend analysis of twitter after the great east japan earthquake
1. Human-centered computing
  1. Collaborative and social computing
    1. Collaborative and social computing theory, concepts and paradigms
      1. Computer supported cooperative work
  2. Human computer interaction (HCI)
    1. Interaction paradigms
      1. Web-based interaction
2. Social and professional topics
  1. Professional topics
    1. Computing and business
      1. Computer supported cooperative work

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Published In

CSCW '12: Proceedings of the ACM 2012 conference on Computer Supported Cooperative Work Companion

February 2012

388 pages

ISBN:9781450310512

DOI:10.1145/2141512

General Chairs:
Steven Poltrock
Boeing Research & Technology
,
Carla Simone
University of Milano-Biccoca
,
Program Chairs:
Jonathan Grudin
Microsoft Research
,
Gloria Mark
University of California, Irvine
,
John Riedl
University of Minnesota

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

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Published: 11 February 2012

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CSCW '12

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SIGCHI

CSCW '12: Computer Supported Cooperative Work

February 11 - 15, 2012

Washington, Seattle, USA

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Overall Acceptance Rate 2,235 of 8,521 submissions, 26%

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October 18 - 22, 2025

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Ma YZhou QTag BSarsenbayeva ZKnibbe JGoncalves J(2023)“Hello, Fellow Villager!”: Perceptions and Impact of Displaying Users’ Locations on WeiboHuman-Computer Interaction – INTERACT 202310.1007/978-3-031-42286-7_29(511-532)Online publication date: 25-Aug-2023
https://doi.org/10.1007/978-3-031-42286-7_29
Contreras DWilkinson SAktas YFallou LBossu RLandès M(2022)Intensity-Based Sentiment and Topic Analysis. The Case of the 2020 Aegean EarthquakeFrontiers in Built Environment10.3389/fbuil.2022.8397708Online publication date: 1-Mar-2022
https://doi.org/10.3389/fbuil.2022.839770
Contreras DWilkinson SAlterman EHervás J(2022)Accuracy of a pre-trained sentiment analysis (SA) classification model on tweets related to emergency response and early recovery assessment: the case of 2019 Albanian earthquakeNatural Hazards10.1007/s11069-022-05307-w113:1(403-421)Online publication date: 23-Mar-2022
https://doi.org/10.1007/s11069-022-05307-w
Ertuncay DCataldi LCosta G(2021)Web-based macroseismic intensity study in Turkey – entries on Ekşi SözlükGeoscience Communication10.5194/gc-4-69-20214:1(69-81)Online publication date: 22-Feb-2021
https://doi.org/10.5194/gc-4-69-2021
Shen SHuang JCheng CZhang TMurzintcev NGao P(2021)Spatiotemporal Evolution of the Online Social Network after a Natural DisasterISPRS International Journal of Geo-Information10.3390/ijgi1011074410:11(744)Online publication date: 2-Nov-2021
https://doi.org/10.3390/ijgi10110744
Ptaszynski MMasui FFukushima YOikawa YHayakawa HMiyamori YTakahashi KKawajiri S(2021)Deep Learning for Information Triage on TwitterApplied Sciences10.3390/app1114634011:14(6340)Online publication date: 8-Jul-2021
https://doi.org/10.3390/app11146340
Amara AHadj Taieb MBen Aouicha M(2021)Multilingual topic modeling for tracking COVID-19 trends based on Facebook data analysisApplied Intelligence10.1007/s10489-020-02033-351:5(3052-3073)Online publication date: 13-Feb-2021
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Umar PAkiti CSquicciarini ARajtmajer S(2021)Self-disclosure on Twitter During the COVID-19 Pandemic: A Network PerspectiveMachine Learning and Knowledge Discovery in Databases. Applied Data Science Track10.1007/978-3-030-86514-6_17(271-286)Online publication date: 13-Sep-2021
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Matsumura NMiura AKomori MHiraishi K(2020)Media Mediate SentimentsMedia Controversy10.4018/978-1-5225-9869-5.ch030(513-527)Online publication date: 2020
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Oldring AMilekhina ABrand A(2020)Tweeting Tsunami: Influence and Early Warning in British ColumbiaCanadian Journal of Communication10.22230/cjc.2020v45n3a358745:3(387-409)Online publication date: 23-Sep-2020
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Information sharing on Twitter during the 2011 catastrophic earthquake

Earthquake shakes Twitter users: real-time event detection by social sensors

Great east Japan earthquake viewed from a URL shortener

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