Intelligent Disaster Response via Social Media Analysis A Survey

Association for Computing Machinery (ACM) - Tập 19 Số 1 - Trang 46-59 - 2017
Tahora H. Nazer1, Guoliang Xue1, Yusheng Ji2, Huan Liu1
1Arizona State University, USA
2National Institute of Informatics, Japan

Tóm tắt

The success of a disaster relief and response process is largely dependent on timely and accurate information regarding the status of the disaster, the surrounding environment, and the a ected people. This information is primarily provided by rst responders on-site and can be enhanced by the firsthand reports posted in real-time on social media. Many tools and methods have been developed to automate disaster relief by extracting, analyzing, and visualizing actionable information from social media. However, these methods are not well integrated in the relief and response processes and the relation between the two requires exposition for further advancement. In this survey, we review the new frontier of intelligent disaster relief and response using social media, show stages of disasters which are reflected on social media, establish a connection between proposed methods based on social media and relief efforts by rst responders, and outline pressing challenges and future research directions.

Từ khóa


Tài liệu tham khảo

Disasters and emergencies: Definitions. http://apps.who.int/disasters/repo/7656.pdf 2002. accessed 09 Jan 2017. Disasters and emergencies: Definitions. http://apps.who.int/disasters/repo/7656.pdf 2002. accessed 09 Jan 2017.

Twitter, 2017, Mar. 17, 2011. accessed

Hurricane, 2016, Oct. 9

Allan J., 1998, Proceedings of the DARPA Broadcast News Transcription and Understanding Workshop, 194

10.1109/WI-IAT.2010.63

10.1111/coin.12017

10.1145/1772690.1772698

Becker H., 2011, ICWSM, 655

Becker H., 2011, Beyond trending topics: Real-world event identification on twitter, ICWSM, 11, 438

10.1007/978-3-319-30319-2_13

Blei D. M., 2003, Latent dirichlet allocation. Journal of machine Learning research, 3(Jan):993--1022

10.1111/j.1745-5871.2009.00611.x

10.5210/fm.v17i4.3937

10.1017/CBO9781316476840

10.1016/j.cag.2013.10.008

10.1145/1871437.1871535

10.1145/1920261.1920265

Cleveland R. B., 1990, Stl: A seasonal-trend decomposition procedure based on loess, Journal of Official Statistics, 6, 3

10.1111/j.0956-7976.2004.00741.x

Cordeiro M., 2012, Doctoral Symposium on Informatics Engineering

10.1007/978-3-319-41706-6_1

10.1201/b11890

10.1109/TVCG.2011.239

10.1037/11503-000

Dynes R. R., 1970, Organized behavior in disaster

Ellis E., 2015, Oct. 7

10.1145/2622633

Faulkner M., 2011, Information Processing in Sensor Networks (IPSN), 2011 10th International Conference on, 13

Gupta A., 2013, $1.00 per rt #bostonmarathon #prayforboston: Analyzing fake content on twitter. In eCrime Researchers Summit (eCRS), 1

10.1145/2487788.2488033

10.1007/978-3-642-13062-5_6

Han B., 2013, ACL (Conference System Demonstrations), 7

10.5555/857190.857680

10.1109/MIC.2007.125

Hill R., 1962, Families in disaster, 185

10.1111/disa.12092

Imran M., 2015, Processing social media messages in mass emergency: A survey. ACM Computing Surveys (CSUR), 47(4):67:1--67:38

10.1145/2567948.2577034

10.1145/2487788.2488109

Imran M., 2013, Proc. of IS- CRAM

John J. P., 2009, NSDI, 9, 291

Jurafsky D., 2014, Pearson

Jurgens D., 2013, what friends are for: Inferring location in online social media platforms based on social relationships, ICWSM, 13, 273

10.1145/2783258.2783319

Kalyanam S. V. M. C., 2016, Proceedings of the ACM/IEEE Conference on Advances in Social Network Analysis and Mining (ASONAM), 437

Kavner L., 2017, Oct. 31, 2012. accessed

Koh Y., 2017, Mar. 21, 2014. accessed

Kossinets G., 2006, Empirical analysis of an evolving social network. science, 311(5757):88--90

10.1214/aoms/1177729694

Kumar S., 2011, ICWSM, 661

10.1145/2631775.2631814

Lee D. D., 2001, Advances in neural information processing systems, 556

Lee K., 2011, ICWSM, 185

10.1145/2187836.2187871

10.1145/1557019.1557077

10.1145/2339530.2339692

Luckerson V., 2014, Time Magazine

Mahmud J., 2012, Where is this tweet from? inferring home locations of twitter users, ICWSM, 12, 511

10.1145/1807167.1807306

10.1201/b18023

Meier P., 2012, Jul. 2

10.1145/1964858.1964869

Mishne G., 2006, AAAI spring symposium: computational approaches to analyzing weblogs, 155

Morstatter F., 2014, Finding eyewitness tweets during crises. arXiv preprint arXiv:1403.1773

10.1109/ASONAM.2016.7752287

Musaev A., 2014, 11th International Conference Information Systems for Crisis Response and Management (ISCRAM), 677

10.1002/asi.21489

10.1145/2736277.2741688

Okolloh O., 2009, or testimony: Web 2.0 tools for crowdsourcing crisis information. Participatory learning and action, 59(1):65--70

Olteanu A., 2014, ICWSM, 376

10.1126/science.aag2579

10.1126/science.aag2579

Palm B., 2016, Oct. 6

10.1145/1871437.1871751

10.1145/1963192.1963246

Powell J. W., 1954, Univ. of Maryland: Disaster Research Project

10.1007/978-3-319-11818-5_19

10.5210/fm.v19i1.4848

10.1145/1963192.1963301

Reese A., 2016, predict the next natural disaster: Advances in natural hazard forecasting could help keep more people out of harm's way, Discover Magazine

10.1145/1772690.1772777

N., 2013, Oct. 3

10.1109/ICDMW.2015.147

Schramm D., 1986, Aim & scope of disaster management: Study guide and course text

Schulz A., 2013, ICWSM, 573

J. SEDERHOLM., 2017, Nov. 14, 2015. accessed

10.1061/(ASCE)1527-6988(2000)1:2(119)

Starbird K., 2010, Proceedings of the 7th International ISCRAM Conference--Seattle, 1

10.1145/1787234.1787254

Temnikova I., 2015, IS-CRAM 2015 proceedings of the 12th international conference on information systems for crisis response and management

Thomas K., 2012, LEET

Tumasjan A., 2010, Predicting elections with twitter: What 140 characters reveal about political sentiment, ICWSM, 10, 178, 10.1609/icwsm.v4i1.14009

10.1145/2566486.2568041

Varga I., 2013, ACL (1), 1619

10.1145/1753326.1753486

10.1007/978-3-642-29047-3_28

10.1016/S0893-6080(05)80023-1

10.1145/2806416.2806560

10.1109/ICDM.2013.86

10.1145/1402946.1402979

Yu S., 2012, A survey of prediction using social media. arXiv preprint arXiv:1203.1647

10.1145/2806416.2806535

10.1109/WI-IAT.2009.53

Zook M., 2010, Volunteered geographic information and crowdsourcing disaster relief: a case study of the haitian earthquake. World Medical and Health Policy by Wiley On-line Library, 2(2):7--33

Thông tin
Thông tin xuất bản

Association for Computing Machinery (ACM)

Tập 19 Số 1

46-59

Thông tin tác giả