Intelligent Disaster Response via Social Media Analysis A Survey
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
Becker H., 2011, ICWSM, 655
Becker H., 2011, Beyond trending topics: Real-world event identification on twitter, ICWSM, 11, 438
Blei D. M., 2003, Latent dirichlet allocation. Journal of machine Learning research, 3(Jan):993--1022
Cleveland R. B., 1990, Stl: A seasonal-trend decomposition procedure based on loess, Journal of Official Statistics, 6, 3
Cordeiro M., 2012, Doctoral Symposium on Informatics Engineering
Dynes R. R., 1970, Organized behavior in disaster
Ellis E., 2015, Oct. 7
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
Han B., 2013, ACL (Conference System Demonstrations), 7
Hill R., 1962, Families in disaster, 185
Imran M., 2015, Processing social media messages in mass emergency: A survey. ACM Computing Surveys (CSUR), 47(4):67:1--67:38
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
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
Kumar S., 2011, ICWSM, 661
Lee D. D., 2001, Advances in neural information processing systems, 556
Lee K., 2011, ICWSM, 185
Luckerson V., 2014, Time Magazine
Mahmud J., 2012, Where is this tweet from? inferring home locations of twitter users, ICWSM, 12, 511
Meier P., 2012, Jul. 2
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
Musaev A., 2014, 11th International Conference Information Systems for Crisis Response and Management (ISCRAM), 677
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
Palm B., 2016, Oct. 6
Powell J. W., 1954, Univ. of Maryland: Disaster Research Project
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
N., 2013, Oct. 3
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
Starbird K., 2010, Proceedings of the 7th International ISCRAM Conference--Seattle, 1
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
Varga I., 2013, ACL (1), 1619
Yu S., 2012, A survey of prediction using social media. arXiv preprint arXiv:1203.1647
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