research-article

Analysis of Microblog Rumors and Correction Texts for Disaster Situations

Authors:

Akiyo Nadamoto,

Eiji AramakiAuthors Info & Claims

IIWAS '13: Proceedings of International Conference on Information Integration and Web-based Applications & Services

Pages 44 - 52

https://doi.org/10.1145/2539150.2539184

Published: 02 December 2013 Publication History

Abstract

Microblogging systems such as Twitter have become popular. They are especially useful and helpful for users in disaster situations. Microblogs have facilitated the spread of information of all kinds, even rumors. Rumors block adequate information sharing and cause severe problems. Several studies have analyzed rumors, but it remains unclear how rumors are spread on microblogging systems. As described in this paper, we present a case study of how rumors are spread on Twitter in a recent disaster situation, that of the Great East Japan earthquake in March 11 2011, based on comparison to a normal situation. We also specifically examine the correction of rumors because automatic extraction of rumors is difficult, but extracting rumor-correction is easier than extracting the rumors themselves. We (1) classify tweets in disaster situations, (2) analyze tweets in disaster situations based on user's impression, and (3) compare the spread of rumor tweets in a disaster situation to that in a normal situation.

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References

[1]

F. Abel, C. Hauff, G. Houben, and et al. Twitchdent:fighting fire with information from social web stream. In Proc of International Conference on Hypertext and Social Media, pages 305--308, 2012.

Digital Library

[2]

G. W. Allport and L. Postman. The Psychology of rumor. Henry Holt, 1947.

[3]

E. Aramaki, S. Maskawa, and M. Morita. Twitter catches the flu: Detecting influenza epidemics using twitter. In Proc. of InternationalConference on Empirical Methods in Natural Language Processing (EMNLP), pages 1568--1576, 2011.

Digital Library

[4]

M. D. Back, A. C. P. Kufner, and B. Egloff. The emotional timeline of September 11, 2001. Psychological Science, 21(10), pages 1417--1419, 2010.

[5]

M. Cohn, M. Mehl, and J. Pennebaker. Linguistic markers of psychological change surrounding September 11, 2001. Psychological Science 15, pages 687--693, 2004.

[6]

R. H. Knapp. A psychology of rumor. Public Opinion Quarterly, 1944.

[7]

B. Longueville, R. Smith, and G. Luraschi. "omg, gtom here, ican see the flames!": A use case of mining location based social networks to acquire spatio-temporal data on forest fires. In Proc of International Workshop on Location Based Socual Networks(LBSN '09), pages 73--80, 2009.

Digital Library

[8]

M. Mendoza, B. Poblete, and C. Castillo. Twitter under crisis: Can we trust what we rt? In Proc. of 1st Workshop on Social Media Analysis(SOMA '10), pages 71--79, 2010.

Digital Library

[9]

NECBIGLOBE. http://www.biglobe.co.jp/pressroom/release/2011/04/27-1. 2011.

[10]

M. Paul and M. Dredze. You are what you tweet: Analyzing twitter for public health. In Proc of Fifth International AAAI Conference on Weblogs and Social Media (ICWSM), pages 265--272, 2011.

[11]

V. Qazvinian, E. Rosengren, D. R. Radev, and Q. Mei. Rumor has it: Identifying misinformation in microblogs. In Proc. of 2011 Conference on Empirical Methods in Natural Language Processing(EMNLP'2011), pages 1589--1599, 2011.

Digital Library

[12]

Y. Qu, C. Huang, P. Zhang, and et al. Microblogging after a major disaster in china: A case study of the 2010 yushu earthquake. In Proc. of ACM 2011 Conference on Computer Supported Cooperative Work (CSCW '11), pages 25--34, 2011.

Digital Library

[13]

Y. Qu, P. Wu, and X. Wang. Online community response to major disaster: A study of tianya forum in the 2008 sichuan earthquake. In 23nd Hawaii International Conference on Systems Science(HICSS-42 2009), pages 1--11, 2009.

Digital Library

[14]

R. L. Rosnow, J. L. Esposito, and L. Gibney. Factors influencing rumor spreading: Replication and extension. Language and Communication, 1988.

[15]

M. Sakaki, Y. Okazaki, and Y. Matsuo. Earthquake shakes twitter users: real-time event detection by social sensors. In Proc. international conference on World Wide Web (WWW), pages 851--860, 2010.

Digital Library

[16]

S. Vieweg, A. Hughes, K. Starbird, and L. Palen. Microblogging during two natural hazards events: what twitter may contribute to situational awareness. In Proc. of 28th International Conference on Human Factors in Computing Systems (CHI '10), pages 1079--1088, 2010.

Digital Library

Cited By

Yamamoto FKumamoto TSuzuki YNadamoto A(2022)Methods of Calculating Usefulness Ratings of Behavioral Facilitation Tweets in Disaster SituationsProceedings of the 11th International Symposium on Information and Communication Technology10.1145/3568562.3568651(88-95)Online publication date: 1-Dec-2022
https://dl.acm.org/doi/10.1145/3568562.3568651
Ochiai KAkatsuka HYamada WTerada M(2021)Disaster Damage Estimation from Real-time Population Dynamics using Graph Convolutional Network (Industrial Paper)Proceedings of the 29th International Conference on Advances in Geographic Information Systems10.1145/3474717.3483972(336-345)Online publication date: 2-Nov-2021
https://dl.acm.org/doi/10.1145/3474717.3483972
Zhao ZZhu XHan TChang C(2021)The fake news propagate with more self-loop in online social networks2021 IEEE 2nd International Conference on Information Technology, Big Data and Artificial Intelligence (ICIBA)10.1109/ICIBA52610.2021.9688320(839-843)Online publication date: 17-Dec-2021
https://doi.org/10.1109/ICIBA52610.2021.9688320
Show More Cited By

Index Terms

Analysis of Microblog Rumors and Correction Texts for Disaster Situations
1. Applied computing
  1. Document management and text processing
2. Information systems
  1. Information systems applications

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cover image ACM Other conferences

IIWAS '13: Proceedings of International Conference on Information Integration and Web-based Applications & Services

December 2013

753 pages

ISBN:9781450321136

DOI:10.1145/2539150

Copyright © 2013 ACM.

Permission to make digital or hard copies of all or part 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 components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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@WAS: International Organization of Information Integration and Web-based Applications and Services

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 02 December 2013

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IIWAS '13

IIWAS '13: The 15th International Conference on Information Integration and Web-based Applications & Services

December 2 - 4, 2013

Vienna, Austria

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Cited By

Yamamoto FKumamoto TSuzuki YNadamoto A(2022)Methods of Calculating Usefulness Ratings of Behavioral Facilitation Tweets in Disaster SituationsProceedings of the 11th International Symposium on Information and Communication Technology10.1145/3568562.3568651(88-95)Online publication date: 1-Dec-2022
https://dl.acm.org/doi/10.1145/3568562.3568651
Ochiai KAkatsuka HYamada WTerada M(2021)Disaster Damage Estimation from Real-time Population Dynamics using Graph Convolutional Network (Industrial Paper)Proceedings of the 29th International Conference on Advances in Geographic Information Systems10.1145/3474717.3483972(336-345)Online publication date: 2-Nov-2021
https://dl.acm.org/doi/10.1145/3474717.3483972
Zhao ZZhu XHan TChang C(2021)The fake news propagate with more self-loop in online social networks2021 IEEE 2nd International Conference on Information Technology, Big Data and Artificial Intelligence (ICIBA)10.1109/ICIBA52610.2021.9688320(839-843)Online publication date: 17-Dec-2021
https://doi.org/10.1109/ICIBA52610.2021.9688320
Chua APal AGoh D(2020)Collective Sensemaking and Location-Related Factors in the Context of a Brand-Related Online RumorDigital Libraries at Times of Massive Societal Transition10.1007/978-3-030-64452-9_25(271-286)Online publication date: 26-Nov-2020
https://doi.org/10.1007/978-3-030-64452-9_25
Yoneda YSuzuki YNadamoto A(2019)Detection of Behavioral Facilitation information in Disaster SituationProceedings of the 21st International Conference on Information Integration and Web-based Applications & Services10.1145/3366030.3366129(255-259)Online publication date: 2-Dec-2019
https://dl.acm.org/doi/10.1145/3366030.3366129
Zannettou SSirivianos MBlackburn JKourtellis N(2019)The Web of False InformationJournal of Data and Information Quality10.1145/330969911:3(1-37)Online publication date: 7-May-2019
https://dl.acm.org/doi/10.1145/3309699
Turenne N(2018)The rumour spectrumPLOS ONE10.1371/journal.pone.018908013:1(e0189080)Online publication date: 19-Jan-2018
https://doi.org/10.1371/journal.pone.0189080
Mizuka KSuzuki YNadamoto A(2018)Extracting Japanese Behavioral Facilitation Tweet in Disaster SituationsProceedings of the 20th International Conference on Information Integration and Web-based Applications & Services10.1145/3282373.3282852(302-305)Online publication date: 19-Nov-2018
https://dl.acm.org/doi/10.1145/3282373.3282852
Chua AAricat RGoh D(2017)Message content in the life of rumors: Comparing three rumor types2017 Twelfth International Conference on Digital Information Management (ICDIM)10.1109/ICDIM.2017.8244643(263-268)Online publication date: Sep-2017
https://doi.org/10.1109/ICDIM.2017.8244643
Liu YJin XShen HCheng X(2017)Do Rumors Diffuse Differently from Non-rumors? A Systematically Empirical Analysis in Sina Weibo for Rumor IdentificationAdvances in Knowledge Discovery and Data Mining10.1007/978-3-319-57454-7_32(407-420)Online publication date: 23-Apr-2017
https://doi.org/10.1007/978-3-319-57454-7_32
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