Building a Natural Disaster Management System based on Blogging Platforms

Full Text (PDF, 701KB), PP.32-39

Views: 0 Downloads: 0


M.V.Sangameswar 1,* M.Nagabhushana Rao 2 M.Shiva Kumar 3

1. Rayalaseema University, Kurnool, Andhra Pradesh, India

2. CSE Department, K.L.University, Vijayawada, Andhra Pradesh, India

3. CSE Department, Trinity College of Engineering and Technology, Karimnagar Telangana

* Corresponding author.


Received: 27 Feb. 2016 / Revised: 12 May 2016 / Accepted: 25 Jul. 2017 / Published: 8 Aug. 2017

Index Terms

Emergency services, Twitter, Google map API, named entity recognizer, gazetteer database, user search methods


Over the decades, numerous kinds of knowledge discovering and sharing of the data techniques are playing a major role to reach the information quickly. Among these since last few years, social networks or media and own blogging are playing a major in sharing the personal information, updating the status, tagging the location and many more features. These data are considered to examine and the acceptance for emergency services to respond with the information gathered from the social network. Taking this into the consideration, proposed an algorithm to find out the location of the person based upon the information shared. This is implemented on a most popular social media twitter to identify the tweets.

Cite This Paper

M.V.Sangameswar, M.Nagabhushana Rao, M.Shiva Kumar, "Building a Natural Disaster Management System based on Blogging Platforms", International Journal of Modern Education and Computer Science(IJMECS), Vol.9, No.8, pp.32-39, 2017. DOI:10.5815/ijmecs.2017.08.05


[1]Gelernter, Judith and Nikolai Mushegian. "Geo-parsing messages from microtext." Transactions in GID 15.6 (2011): 753-773.
[2]Jaiswal, Anuj, Wei Peng and Tong Sun. "Predicting Time-sensitive User Locations from Social Media." 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining. New York: ACM, 2013. 870-877.
[3]Zhang, Wei and Judith Gelernter. "Geocoding location expressions in Twitter messages: A preference learning method." Journal of Spatial Information Science 9 (2015): 37-70.
[4]Nadeau, David and Satoshi Sekine. "A survey of named entity recognition and classification." National Research Council Canada (2006).
[5]Leidner, Jochen L. and Michael D. Lieberman. "Detecting Geographical References in the Form of Place Names and Associated Spatial Natural Language." SIGSPATIAL Special 3.2 (2011): 5-11.
[6]Wang, Wei. "Automated spatiotemporal and semantic information extraction for hazards." PhD (Doctor of Psychology) thesis, University of Iowa (2014).
[7]Kinsella, Sheila, Vanessa Murdock and Neil O'Hare. "I'm eating a sandwich in Glasgow: modeling locations with Tweets." Proceedings of the 3rd international workshop on Search and mining user-generated contents. ACM (2011).
[8]Starbitd, Kate and Stamberger, Jeannie. “Tweak the Tweet: Leveraging Microblogging Proliferation with a Prescriptive Syntax to Support Citizen Reporting”. Proceedings of the 7th International ISCRAM Conference – Seattle, USA (2010).
[9]HAN, B., COOK, P., AND BALDWIN, T. Text-based twitter user geolocation prediction. Journal of Artificial Intelligence Research 49 (2014), 451–500. doi:10.1613/jair.4200.
[10]AHLERS, D. Assessment of the accuracy of geonames gazetteer data. In Proc. 7th Workshop on Geographic Information Retrieval (Orlando, Florida, 2013), ACM, pp. 74–81. doi:10.1145/2533888.2533938.
[11]EDWARDS, S. E., STRAUSS, B., AND MIRANDA, M. L. Geocoding large populationlevel administrative datasets at highly resolved spatial scales. Transactions in GIS 18, 4 (2013), 586–603. doi:10.1111/tgis.12052.
[12]GELERNTER, J., AND BALAJI, S. An algorithm for local geoparsing of microtext. GeoInformatica 17, 4 (2013), 635–667. doi:10.1007/s10707-012-0173-8
[13]GELERNTER, J., GANESH, G., KRISHNAKUMAR, H., AND ZHANG, W. Automatic gazetteer enrichment with user-geocoded data. In Proc. 2nd ACM SIGSPATIAL International Workshop on Crowdsourced and Volunteered Geographic Information (New York, NY, 2013), pp. 87–94. doi:10.1145/2534732.2534736.
[14]GELERNTER, J., AND ZHANG, W. Cross-lingual geo-parsing for non-structured data. In Proc. 7th Workshop on Geographic Information Retrieval (New York, NY, 2013), ACM, pp. 64–71. doi:10.1145/2533888.2533943.
[15]SPERIOSU, M., AND BALDRIDGE, J. Text-driven toponym resolution using indirect supervision. In Proc. 51st Annual Meeting of the Association for Computational Linguistics (ACL) (Sofia, Bulgaria, 2013), pp. 1466–1476.