Chihiro Maru
ABSTRACT
When the Great East Japan Earthquake occurred in 2011, it was difficult to immediately grasp all telecommunications network conditions using only information from network monitoring devices because the damage was considerably heavy and a severe congestion control state occurred. Moreover, at the time of the earthquake, telephone and e-mail services could not be used in many cases-although social networking services (SNSs) were still available. In an emergency, such as an earthquake, users proactively convey information on telecommunications network conditions through SNSs. Therefore the collective intelligence of SNSs is suitable as a means of information detection complementary to conventional observation through network monitoring devices. In this paper, we propose a network failure detection system that detects telephony failures with a high degree of accuracy by using the collective intelligence of Twitter, one of the most widely used SNSs. We also show that network control can be performed automatically and autonomically using information on telecommunications network conditions detected with our system.
- "Great East Japan Earthquake - Wikipedia, March 2011", https://en.wikipedia.org/wiki/2011_Tohoku_earthquake_and_tsunamiGoogle Scholar
- K. Kagawa, Y. Kuno, H. Tamura, H. Takada, M. Furutani, and N. Minamikata, "Improvement of Credibility for Operation System in the Case of Large Disaster," NTT DOCOMO technical journal, vol.20, no.4, pp.26-36, 2013.Google Scholar
- ITU-T Forcus Group on Disaster Relief Systems, "Monitoring Systems for Outside Plant Facilities," ITU-T Recommendations, no.L.81, pp.1-10, 2009.Google Scholar
- A. Acar, and Y. Muraki, "Twitter for Crisis Communication: Lessons Learned from Japan's Tsunami Disaster," International Journal of Web Based Communities, vol.7, no.3, pp.392-402, 2011. Google ScholarDigital Library
- "Twitter," http://twitter.com/Google Scholar
- T. Sakaki, M. Okazaki, and Y. Matsuo, "Earthquake Shakes Twitter Users: Real-Time Event Detection by Social Sensors," Proceedings of the 19th International Conference on World Wide Web, pp.851-860, 2010. Google ScholarDigital Library
- National Institute for Land and Infrastructure Management, "A Study on Method for Detection of Disaster Outbreak by Means of Social Media Analysis," https://www.kantei.go.jp/jp/singi/it2/senmonbunka/bousai/dai5/siryou5.pdf, 2014.Google Scholar
- S. Saito, Y. Ikawa, and H. Suzuki, "Early Detection of Disasters with Contextual Information on Twitter," Technical Report of IEICE, vol.114, no.81, pp.7-12, 2014.Google Scholar
- A. Sadilek, H. Kautz, and V. Silenzio, "Predicting Disease Transmission from Geo-Tagged Micro-Blog Data," Proceeding of the 26th AAAI Conference on Artificial Intelligence, pp.136-142, 2012. Google ScholarDigital Library
- P. Metaxas, E. Mustafaraj, and D. Gayo-Avello, "How(not) to Predict Elections," Privacy, Security, Risk and Trust and 2011 IEEE Third International Conference on Social Computing, pp.165-171, 2011.Google Scholar
- S. Verma, S. Vieweg, W. Corvey, L. Palen, J. Martin, M. Palmer, A. Schram, and K. Anderson, "Natural Language Processing to the Rescue?: Extracting "Situational Awareness" Tweets During Mass Emergency," ICWSM, 2011.Google Scholar
- K. Rudra, S. Ghosh, N. Ganguly, P. Goyal, and S. Ghosh, "Extracting Situational Information from Microblogs during Disaster Events: a Classification-Summarization Approach," Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, pp.583-592, 2015. Google ScholarDigital Library
- S. Panem, M. Gupta, and V. Varma, "Structured Information Extraction from Natural Disaster Events on Twitter," Proceedings of the 5th ACM International Workshop on Web-scale Knowledge Representation Retrieval&Reasoning, pp.1-8, 2014. Google ScholarDigital Library
- I. Varga, M. Sano, K. Torisawa, C. Hashimoto, K. Ohtake, T. Kawai, J. Oh, and S. Saeger, "Aid is Out There: Looking for Help from Tweets during a Large Scale Disaster," ACL, pp.1619-1629, 2013.Google Scholar
- T. Sakaki, T. Yanagihara, K. Nawa, and Y. Matsuo, "Driving Information Extraction from Twitter," The IEICE Transactions on Information and Systems, vol.J98-D, no.6, pp.1019-1032, 2015.Google Scholar
- M. Cameron, R. Power, B. Robinson, and J. Yin, "Emergency Situation Awareness from Twitter for Crisis Management," Proceedings of the 21st International Conference Companion on World Wide Web, pp.695-698, 2012. Google ScholarDigital Library
- Y. Qu, C. Huang, P. Zhang, and J. Zhang, "Microblogging after a Major Disaster in China: a Case Study of the 2010 Yushu Earthquake," Proceedings of the ACM 2011 Conference on Computer Supported Cooperative Work, pp.25-34, 2011. Google ScholarDigital Library
- J. Mizuno, J. Goto, K. Ohtake, T. Kawada, K. Torizawa, J. Kloetzer, M. Tanaka, C. Hashimoto, and A. Okumura, "Performance Evaluation of Disaster Information Analysis System DISAANA and its Question Answer Mode," IPSJ Consumer Device&System (CDS), vol.2015-CDS-14, no.14, pp.1-13, 2015.Google Scholar
- T. Qiu, J. Feng, Z. Ge, J. Wang, J. Xu, and J. Yates, "Listen to Me if You can: Tracking User Experience of Mobile Network on Social Media," Proceedings of the 10th ACM SIGCOMM Conference on Internet Measurement, pp.288-293, 2010. Google ScholarDigital Library
- K. Takeshita, M. Yokota, K. Nishimatsu, and Haruhisa Hasegawa, "Proposal of the Network Failure Information Acquisition Method from Social Network Services," IEICE Society Conference 2012, B-7-35, 2012.Google Scholar
- K.Takeshita, M.Yokota, K. Nishimatsu, and H. Hasegawa, "Evaluation of the Network Failure Information Acquisition System from Social Network Services," Proceedings of the 2013 IEICE General Conference, B-7-44, 2013.Google Scholar
- K. Takeshita, M. Yokota, and K. Nishimatsu, "Early Network Failure Detection System by Analyzing Twitter Data," IFIP/IEEE International Symposium on, pp.279-286, 2015.Google Scholar
- K. Kurihara, K. Shimada, "Bug sentence extraction from Twitter using the bootstrap method," Natural Language Processing 2015, pp.341-344, 2015.Google Scholar
- "Twitter Serch API," https://dev.twitter.com/rest/public/searchGoogle Scholar
- "MeCab," http://mecab.sourceforge.net/Google Scholar
- "Yahoo! reverseGeoCoder API," http://developer.yahoo.co.jp/webapi/map/openlocalplatform/v1/reversegeocoder.htmlGoogle Scholar
- T. Joachims, "Making Large Scale SVM Learning Practical," 1999.Google Scholar
- A. Nakao, "Software-De ned Data Plane Enhancing SDN and NFV," Special Section on Quality of Diversifying Communication Networks and Services, IEICE Transactions on Communications, vol.E98-B, no.1, pp.12-19, 2015.Google Scholar
- "JGN-X," http://www.jgn.nict.go.jp/english/Google Scholar
Index Terms
- Development of Failure Detection System for Network Control using Collective Intelligence of Social Networking Service in Large-Scale Disasters
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