Abstract
Similar to Twitter, Weibo is a popular Chinese microblogging service that is used to read and write millions of short text messages on any topic within 140-character limit. Users create status messages, which sometimes show opinions about different subjects. Particularly, after a disaster, people always express their states and emotions toward the situation via microblogging service. The previous study works revealed that public negative emotions could be associated with the subsequent incidents. Therefore, once a disaster happens, the crowed negative sentiment among victims needs to be paid more attention, which could be useful to discover the following emergency events such as public fear and crisis. In order to detect potential incidents implicated by victims’ negative emotions in the post-disaster situation, this paper proposes a structured framework including three phases. The first phase focuses on how to identify disaster-related Weibo messages from the massive and noisy microblogging stream, and the second phase is about how to filter negative sentiment messages from all of the disaster-concerned microblogging. We introduced machine learning methods into both of the above phases. In the last phase, we pay attention on crowd negative sentiment, by tracking and predicting victims’ negative emotions changing trend on the base of GM (1, 1) to carry out incidents discovery in a post-disaster situation. By the case study of Ya’an earthquake, we demonstrated that the proposed framework could perform well in incidents monitors such as aftershocks and potential public crisis, which is meaningful and useful to disaster relief process and emergency management in post-disaster situation.
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Abel F, Hauff C, Houben G-J, Stronkman R, Tao K (2012) Twitcident: fighting fire with information from social web stream. In: Proceedings of the 23rd ACM conference on hypertext and social media
Abel F, Hauff C, et al (2012) Twitcident: fighting fire with information from social web streams. In: Proceedings of the 21st international conference companion on World Wide Web,ACM, Lyon, pp 305–308
Aisopos F, et al (2012) Cotent vs context for sentiment analysis: a comparative analysis over microblogs. In: The 23rd ACM conference on hypertext and social media, pp 96–187
Alexander DE (2014) Social media in disaster risk reduction and crisis management. Sci Eng Ethics 20(3):717–733
Austin L, Liu BF, Jin Y (2012) How audiences seek out crisis information: exploring the social-mediated crisis communication model. J Appl Commun Res 40(2):188–207
Avvenuti M, et al (2014) EARS (earthquake alert and report system): a real time decision support system for earthquake crisis management. In: The 20th ACM SIGKDD international conference on knowledge discovery and data mining, KDD’14, New York, pp 1749–1758
Barbosa L, Feng J (2010) Robust sentiment detection on Twitter from biased and noisy data. In: International conference on computational linguistics, pp 36–44
Bella R, et al (2014) Developing a Sina Weibo incident monitor for disasters. In: Proceedings of the Australian language technology association workshop
Cameron M, Power P, Robinson B, Yin J (2012) Emergency situation awareness from Twitter for crisis management. In: Proceedings of SWDM 2012 workshop held jointly with WWW
Castillo CM, Mendoza BP (2013) Predicting information credibility in time-sensitive social media. Internet Res 23(5):560–588
Cheng J et al (2011) An information diffusion based recommendation framework for micro-blogging. J As Inf Syst 12(7):45–73
Chowdhury S, et al (2013) Tweet4act: using incident-specific profiles for classifying crisis-related messages. In: Proceedings of the ISCRAM, Baden–Baden, Germany, pp 834–839)
Chowdhury SR, Imran, M, Asghar MR, Amer-Yahia S, Castillo C (2013) Tweet4act: using incident-specific profiles for classifying crisis-related messages. In: The 10th international conference on information systems for crisis response and management, ISCRAM
Earle PS, Bowden DC, Guy M (2011) Twitter earthquake detection: earthquake monitoring in a social world. Ann Geophys 54(6):708–715
Google (2013). https://code.google.com/p/word2vec/
Heath RL, Palenchar MJ, O’Hair HD (2009) Community building through risk communication infrastructure. In: Heath RL, O'Hair HD (eds) Handbook of risk and crisis communication. Taylor & Francies Group, New York, pp 471–487
Hinton GE (1986) Learning distributed representations of concepts. In: Proceedings of the 8th Annual Conference of the Cognitive Science Society, pp 1–12
Hua B et al (2015) Sina Weibo incident monitor and Chinese disaster microblogging classification. J Digit Inf Manag 13(3):156–161
Hughes AL., Palen TL (2009) Twitter adoption and use in mass convergence and emergency events. In: Proceedings of the 6th international ISCRAM conference
Lee HA, et al (2015) Social media in emergency management. FEMA in higher education program. In: Issues in disaster science and management: a critical dialogue between scientists and emergency managers
Li J, Rao HR (2010) Twitter as a rapid response news service: an exploration in the context of the 2008 China earthquake. Electron J Inf Syst Dev Ctries 42(4):1–22
Ma H, Chang Y, Chen Z Flooding in seven days: Yuyao’s red and black. (2013-10-14) (2013-10-16). http://www.kcis.cn/4409
Mandel B, et al (2012) A demographic analysis of online sentiment during hurricane Irene. In: NAACL-HLT workshop on language in social media
Mendoza M, Poblete B, Castillo C (2010) Twitter under crisis: can we trust what we RT? In: Proceedings of the first workshop on social media analytics, pp 1–9
Mostafa MM (2013) More than words: social networks’ text mining for consumer brand sentiments. Expert Syst Appl 40(10):4241–4251
Nagy A, Stamberger J (2012) Crowd sentiment detection during disasters and crises. In: 9th International conference on information systems for crisis response and management, ISCRAM
Neviarouskaya A, Prending H, Ishizuks M (2011) Affect analysis model: novel rule-based approach to affect sensing from text. Int J Nat Lang Eng 17(1):95–135
Oh O, Kwon KH, Rao HR (2010) An exploration of social media in extreme events: rumor theory and twitter during the Haiti earthquake 2010. In: proceedings of the international conference on information systems pp 1–14
Olteanu A, et al (2015) What to expect when the unexpected happens: social media communications across crises. In: Proceedings of computer supported cooperative work, CSWC
Omidyar Network. Ushahidi: The African Software Platform Helping Victims in Global Emergencies.(2013-1-22) [2013-7-08]. http://www.ushahidi.com/
Pang B, Lee L (2008) Opinion mining and sentiment analysis, foundations and trends in information retrieval, pp 1–120
Qu Y, Huang C, Zhang P, Zhang J (2011) Microblogging after a major disaster in China: a case study of the 2010 Yushu earthquake. In: Proceedings of the ACM 2011 conference on computer supported cooperative work, CSCSW, pp 25–34
Robinson B, Power R, Cameron M (2013) An evidence based earthquake detector using Twitter. In: Proceedings of the workshop on language processing and crisis information (LPCI), Nagoya, Japan, pp 1–9
Rogstadius J et al (2013) Crisistracker: crowdsourced social media curation for disaster awareness. IBM J Res Dev 57(5):411–413
Rowe M, Angeletou S, Alani H (2011) Predicting discussions on the social semantic Web. In: Proceedings of the 8th extended semantic Web conference on the semantic Web: research and applications pp 1–15
Sakai T, Tamura K (2014) Identifying bursty areas of emergency topics in geotagged tweets using density-based spatiotemporal clustering algorithm. In: Proceedings of the 16th international workshop on combinatorial image analysis (IWCIA 2014), pp 95–100
Sakai T, Okazaki M, Matsuo Y (2010) Earthquake shakes Twitter users: real-time event detection by social sensors. In: Proceedings of the 19th international conference on World Wide Web, pp 851–860. ACM
Sakai T, Okazaki M, Matsuo Y (2013) Tweet analysis for real-time event detection and earthquake reporting system development. IEEE Trans Knowl Data Eng 25(4):919–931
Sina Company. Weibo Reports Third Quarter 2015 Results. (2015-11-19) [2015-11-20]. http://weibo.com
Takahashi T Igata N (2012) Rumor detection on Twitter. In: Proceedings of 2012 joint 6th international conference on soft computing and intelligent systems (SCIS) and 13th international symposium on advanced intelligent systems (ISIS) 452–457
Thewall M, Buckley K, Paltoglou G (2011) Sentiment in Twitter events. J Am Soc Inf Sci Technol 62(2):406–418
USGS. Did you feel it? (2005-03-21) (2012-09-26) http://earthquake.usgs.gov/earthquakes/dyfi/
Utz S, Schultz F, Glocka S (2013) Crisis communication online: how medium, crisis type and emotions affected public reactions in the Fukushima Daiichi nuclear disaster. Public Relat Rev 39(1):40–46
Vieweg S (2012) Twitter communications in mass emergency: contributions to situational awareness. In: Proceedings of the ACM 2012 conference on computer supported cooperative work companion pp 227–230
Vieweg S, Hughes AL, Starbird K, Palen L (2010) Microblogging during two natural hazards events: what twitter may contribute to situational awareness. In: Proceedings of the SIGCHI conference on human factors in computing systems, pp 1079–1088
Vo BKH, Collier N (2013) Twitter emotion analysis in earthquake situation. IJCLA 4(1):159–173
Xu L et al (2008) Constructing the affective lexicon ontology. J China Soc Sci Tech Inf 27(2):180–185
Yin J, Lampert A, Cameron M, Robinson B, Power R (2012) Using social media to enhance emergency situation awareness. IEEE Intell Syst 27(6):52–59
Zhou X, et al (2013) Classification of microblogs for support emergency responses: case study Yushu earthquake in China. In: The 46th Hawaii international conference on system Sciences, pp 1553–1562
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Bai, H., Yu, G. A Weibo-based approach to disaster informatics: incidents monitor in post-disaster situation via Weibo text negative sentiment analysis. Nat Hazards 83, 1177–1196 (2016). https://doi.org/10.1007/s11069-016-2370-5
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DOI: https://doi.org/10.1007/s11069-016-2370-5