Abstract
Twitter like microblogging site is used by millions of people to share their daily lives. During a natural disaster, the situational updates posted by users will get mixed with millions of other tweets and will be difficult to monitor manually in real time. Also, timely identification of situational updates, along with the location is very important for the rescue and relief operations during the disaster event. The tweets with contextual information posted during disaster provide information regarding the need or availability of resources and services, the number of casualties, infrastructures damage, and warnings or cautions. Some disaster-related tweet may not have any actionable information. This paper presents an alert generation framework, which will intake the tweets posted during the disaster, detects, classifies and geocodes the tweets belonging to each class, which provide actionable information, in order to alert the concerned authorities about the current situation in a timely manner.
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References
Aupetit, M., Imran, M.: Interactive monitoring of critical situational information on social media. In: Social Media Studies Proceedings of the 14th ISCRAM Conference Albi, France (2017)
Imran, M., Castillo, C., Lucas, J., Meier, P., Vieweg, S.: Aidr: artificial intelligence for disaster response. In: Proceedings of WWWcompanion, pp. 159–162 (2014)
Rudra, K., Banerjee, S., Ganguly, N., Goyal, P., Imran, M., Mitra, P.: Summarizing situational tweets in crisis scenario. In: Proceedings of the 27th ACM Conference Hypertext and Social Media. ACM, pp. 137–147 (2016)
Hong, L., Fu, C.,Wu, J., Frias-Martinez, V.: Information needs and communication gaps between citizens and local governments online during natural disasters. Inf. Syst. Front. 20(5) (2018)
Sreenivasulu, M., Sridevi, M.: Mining informative words from the tweets for detecting the resources during disaster. In: International Conference on Mining Intelligence and Knowledge Exploration. Springer International Publishing AG 10682, pp. 348–358 (2017)
Basu, M., Ghosh, S., Ghosh, K., Choudhury, M.: Overview of the FIRE 2017 track: Information retrieval from microblogs during disasters (IRMiDis)—working notes of FIRE 2017. In: CEUR Workshop Proceedings, vol. 2036, pp. 28–33 (2017)
Anbalagan, B., Valliyammai, C.: #ChennaiFloods: leveraging human and machine learning for crisis mapping during disasters using social media. In IEEE 23rd International Conference on High Performance Computing Workshops (2016)
Rudra, K., Ghosh, S., Ganguly, N., Goyal, P., Ghosh, S.: Extracting and summarizing situational information from the twitter social media during disasters. ACM Trans Web 12(3), 17, Article 35 (2018)
Dwivedi, Y.K., Singh, J.P. Rana, N.P., Kapoor, K.K.: Event classification and location prediction from tweets during disasters. Application of or in disaster relief operations. Springer (2017)
Yuzawa, A., Ichikawa, H., kobayashi, A.: Extracting Tweets related to disaster information by using multiple co-occurrence relation of words. In: IEEE International Conference on Smart Computing (2018)
Anirban, S., Rudra, K., Ghosh, S.: Extracting situational awarness from microblogs during disaster events. In: 7th International Conference on Communication Systems and Networks, Social Networking Workshot, CCOMSNETS (2015)
Imran, M., Mitra, P., Castillo, C.: Twitter as a lifeline: human-annotated Twitter corpora for NLP of crisis-related messages. In: Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC), Paris, France (2016)
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Hasla, M., Swaraj, K.P. (2020). Alert Generation Framework from Twitter Data Stream During Disaster Events. In: Smys, S., Senjyu, T., Lafata, P. (eds) Second International Conference on Computer Networks and Communication Technologies. ICCNCT 2019. Lecture Notes on Data Engineering and Communications Technologies, vol 44. Springer, Cham. https://doi.org/10.1007/978-3-030-37051-0_60
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DOI: https://doi.org/10.1007/978-3-030-37051-0_60
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