The DOI matches its Crossref record; 16 of the 16 references it cites were found in a registry or an index.
Twitter for crisis communication: lessons learned from Japan's tsunami disaster
Abstract
Two weeks after the Great Tohoku earthquake followed by the devastating tsunami, we have sent open-ended questionnaires to a randomly selected sample of Twitter users and also analysed the tweets sent from the disaster-hit areas. We found that people in directly affected areas tend to tweet about their unsafe and uncertain situation while people in remote areas post messages to let their followers know that they are safe. Our analysis of the open-ended answers has revealed that unreliable retweets (RTs) on Twitter was the biggest problem the users have faced during the disaster. Some of the solutions offered by the respondents included introducing official hash tags, limiting the number of RTs for each hash tag and adding features that allow users to trace information by maintaining anonymity.
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(2008) · Biostatistics and Microbiology: A Survival Manual · doi:10.1007/978-0-387-77282-0_6 · Cited by 17,371
Qualitative research: Methods in the social sciences: (668352010-001)
(2006) · PsycEXTRA Dataset · doi:10.1037/e668352010-001 · Cited by 10,605
Qualitative data analysis (2nd ed)
Morse, J. M. (1996) · Journal of Psychosomatic Research · doi:10.1016/0022-3999(96)00056-6 · Cited by 5,593
Earthquake shakes Twitter users: real-time event detection by social sensors
Sakaki, T., Okazaki, M., & Matsuo, Y. (2010) · Proceedings of the 19th international conference on World wide web · doi:10.1145/1772690.1772777 · Cited by 3,640
Predicting Elections with Twitter: What 140 Characters Reveal about Political Sentiment
Tumasjan, A., Sprenger, T., Sandner, P., & Welpe, I. (2010) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v4i1.14009 · Cited by 2,649
Why we twitter: understanding microblogging usage and communities
Java, A., Song, X., Finin, T., & Tseng, B. (2007) · Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 workshop on Web mining and social network analysis · doi:10.1145/1348549.1348556 · Cited by 2,645
Predicting the future with social media
Asur, S., & Huberman, B. A. (2010) · 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology · doi:10.1109/wi-iat.2010.63 · Cited by 2,081
Vieweg, S., Hughes, A. L., Starbird, K., & Palen, L. (2010) · Proceedings of the SIGCHI Conference on Human Factors in Computing Systems · doi:10.1145/1753326.1753486 · Cited by 1,427
Twitter under crisis: can we trust what we RT?
Mendoza, M., Poblete, B., & Castillo, C. (2010) · Proceedings of the First Workshop on Social Media Analytics · doi:10.1145/1964858.1964869 · Cited by 955
Social networks that matter: Twitter under the microscope
Huberman, B., Romero, D. M., & Wu, F. (2008) · First Monday · doi:10.5210/fm.v14i1.2317 · Cited by 932
Cited by
Houston, J. B., Hawthorne, J., Perreault, M. F., Park, E. H., Goldstein Hode, M., Halliwell, M. R., Turner McGowen, S. E., Davis, R., Vaid, S., McElderry, J. A., et al. (2015) · Disasters · doi:10.1111/disa.12092 · Cited by 869
Processing Social Media Messages in Mass Emergency: A Survey
Imran, M., Castillo, C., Diaz, F., & Vieweg, S. (2015) · ACM Computing Surveys · doi:10.1145/2771588 · Cited by 804
Applications of artificial intelligence for disaster management
Sun, W., Bocchini, P., & Davison, B. D. (2020) · Natural Hazards · doi:10.1007/s11069-020-04124-3 · Cited by 491
Takahashi, B., Tandoc, E. C., & Carmichael, C. (2015) · Computers in Human Behavior · doi:10.1016/j.chb.2015.04.020 · Cited by 375
Social Media in Crisis Management: An Evaluation and Analysis of Crisis Informatics Research
Reuter, C., Hughes, A. L., & Kaufhold, M.-A. (2018) · International Journal of Human–Computer Interaction · doi:10.1080/10447318.2018.1427832 · Cited by 264
Metadata
- Authors
- Acar, A., & Muraki, Y.
- Venue
- International Journal of Web Based Communities
- Publisher
- Inderscience Publishers
- Published
- 2011
- Volume
- 7
- Issue
- 3
- Pages
- 392
- ISSN
- 1477-8394 (ISSN-L)
- 1741-8216
- OpenAlex
- W1981048805
- Cited by
- 419
- References
- 16
- FWCI
- 55.19
- Type
- journal article
- Language
- en
- Open access
- closed
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