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Reseach Article

Summarization Approach From Microblog During Disaster Events

by Pooja B. Kawade, N. N.Pise, P. V. Kulkarni
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 176 - Number 8
Year of Publication: 2017
Authors: Pooja B. Kawade, N. N.Pise, P. V. Kulkarni
10.5120/ijca2017915621

Pooja B. Kawade, N. N.Pise, P. V. Kulkarni . Summarization Approach From Microblog During Disaster Events. International Journal of Computer Applications. 176, 8 ( Oct 2017), 15-19. DOI=10.5120/ijca2017915621

@article{ 10.5120/ijca2017915621,
author = { Pooja B. Kawade, N. N.Pise, P. V. Kulkarni },
title = { Summarization Approach From Microblog During Disaster Events },
journal = { International Journal of Computer Applications },
issue_date = { Oct 2017 },
volume = { 176 },
number = { 8 },
month = { Oct },
year = { 2017 },
issn = { 0975-8887 },
pages = { 15-19 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume176/number8/28574-2017915621/ },
doi = { 10.5120/ijca2017915621 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:41:58.813614+05:30
%A Pooja B. Kawade
%A N. N.Pise
%A P. V. Kulkarni
%T Summarization Approach From Microblog During Disaster Events
%J International Journal of Computer Applications
%@ 0975-8887
%V 176
%N 8
%P 15-19
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract

During bulk convergence events such as natural disasters, microblogging platforms like Twitter are broadly used by affected people to post situational awareness messages. As soon as natural disaster events happen, users are willing to know more about them. Twitter is a great source that can be exploited for obtaining such fine-grained arranged information for fresh natural disaster events. These crisis-related messages disperse among multiple categories like infrastructure damage, information about bomb blast, missing, injured, and dead people etc. The challenge here is to create summary from disaster related tweets and filter the short spam url containing tweets.

References
  1. Koustav Rudra, Subham Ghosh, Niloy Ganguly, Extracting Situational Information from Microblogs during Disaster Events: a Classification- Summarization Approach. CIKM, Melbourne, VIC, Australia, 2015.
  2. Koustav Rudra, Siddhartha Banerjee, Niloy Ganguly, Summarizing Situational Tweets in Crisis Scenario. HT , Halifax, NS, Canada,2016.
  3. Axel Bruns,Yxian Liang, Tools and Methods for capturing twitter data during natural disaster. In First Monday, Volume 17, Number 4-2April 2013.
  4. Andrei Olariu, Eficient Online Summarization of Microblogging Streams. In Pro-ceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics, pages 236-240,April 26-30 Gothenburg, Sweden, 2014.
  5. Sandeep Panem, Manish Gupta, Vasudeva Varma,Structured Information Extraction from Natural Disaster Events on Twitter. KDD , xian,china,2014.
  6. Zhenhua Wang, Lidan Shou, Ke Chen, Gang Chen, On Summarization and Timeline Generation for Evolutionary Tweet Streams. In IEEE Transactions on Knowledge and Data Engineering, DOI 10.1109/TKDE, 2013.
  7. Muhammad Imran, Fernando Diaz, Carlos Castillo, Processing Social Media Messages in Mass Emergency: A Survey ACM Computing Surveys, Vol. 47, No. 4,Article 67,2015.
  8. Chao Shen, Fei Liu, FuliangWeng,Tao Li, A Participant-based Approach for Event Summarization Using Twitter Streams,KDD,Xian,China,2014.
  9. Muhammad Imran, Carlos Castillo, Ji Lucas, AIDR: Artificial Intelligence for Disaster Response. In WWW Companion, Seoul, Korea,April 7,2014.
  10. Sarah Vieweg, Carlos Castillo, and Muhammad Imran, Integrating Social Media Communications into the Rapid Assessment of Sudden Onset Disasters. In Springer LNCS 8851, pp. 444-461,2014.
  11. Miles Osborne, Elizabeth Cano, Craig Macdonald, Real-Time Detection, Tracking, and Monitoring of Automatically Discovered Events in Social Media,2014.
  12. Alan Ritter, Mausam, Open Domain Event Extraction from Twitter. In KDD,Beijing, China,August 1216, 2012.
  13. Robert Power, Bella Robinson, John Colton, Emergency Situation Awareness:Twitter Case Studies. In Springer ISCRAM-med , pp. 218231, 2014.
  14. Pengyi Zhang, Microblogging after a Major Disaster in China: A Case Study of the 2010 Yushu Earthquake. In CSCW , Hangzhou, China March 19-23, 2011.
  15. https://en.wikipedia.org/wiki/Natural_disaster
  16. Chao Chen, Jun Zhang, Wan lei Zhou, 6 Million Spam Tweets: A Large Ground Truth for Timely Twitter Spam Detection. IEEE ICCCommunication and Information Systems Security Symposium, 2015 .
  17. Shigang Liu, Jun Zhang, Yang Xiang, Statistical Detection of Online Drifting Twitter Spam. ASIA CCS, Xian, China,May 30June 3, 2016.
  18. Xianghan Zheng, Zhipeng Zeng, Zheyi Chen, Detecting spammers on social networks. Neurocomputing159(27-34),2015.
  19. Sangho Lee, Jong Kim, WARNINGBIRD: A Near Real-time Detection System for Suspicious URLs in Twitter Stream. IEEE Transaction On Dependable And Secure Computing, Vol. 10, no. 2, January 2013.
  20. Sandeep Kumar Rawat, Saurabh Sharma,A Review on Spam Classification of Twitter Data Using Text Mining and Content Filtering. In International Journal of Advanced Research in Computer Science and Software Engineering Volume 5,Issue 6, pp. 485-488,June 2015.
  21. Hailu Xu, Weiqing Sun, Ahmad Javaid, Eficient Spam Detection across Online Social Networks.In Big Data Analysis (ICBDA), IEEE International Conference12-14, Hangzhou, China,March 2016 .
  22. Jyoti D. Halwar, Sandeep Kadam, Vrushali Desale,Detection of Suspicious URL in Social Networking Site Twitter: Survey Paper . In International Journal of Computer Applications Volume 110 No. 8, January 2015.
  23. Monika Verma,Sanjeev Sofat, Techniques to Detect Spammers in Twitter- ASurvey. In International Journal of Computer Applications Volume 85 No 10,January 2014.
  24. Guofei Gu, Chao Yang, Amit A. Amleshwaram, CATS: Characterizing Automation of Twitter Spammers.In Fifth International Conference,Bangalore, India,7-10 Jan 2013.
  25. Chao Yang, Robert Harkreader, and Guofei Gu, Empirical Evaluation and New Design for Fighting Evolving Twitter Spammers.In IEEE Transactions on Information Forensics and Security Volume: 8, Issue: 8, Aug. 2013.
  26. Nasim eshraqi, Mehrdad Jalali, Mohammad Hossein Moattar, Detecting Spam Tweets In Twitter Using a Data Stream Clustering Algorithm Second International Congress on Technology, Communication and Knowledge (ICTCK) November, Mashhad Branch, Islamic Azad University, Mashhad, Iran,11-12jan, 2015 .
  27. Cheng Cao and James Caverlee, Detecting Spam URLs in Social Media via Behavioral Analysis. In ECIR, LNCS 9022, pp. 703714, Springer International Publishing Switzerland,2015.
  28. Sangho Lee, Jong Kim, Early filtering of ephemeral malicious accounts on Twitter Pages 48-57, Volume 54, 1 December 2014.
  29. Nikitha.R, Anand.R, Detection of Suspicious URLs through Vision Techniques in Twitter Stream. In International Journal of Advancements in Research and Technology, Volume 3, Issue 5, May-2014.
  30. Nour El-Mawass and Saad Alaboodi, Hunting for Spammers: Detecting Evolved Spammers on Twitter. In arXiv:1512.02573v2 [cs.IR] 15 Dec 2015.
Index Terms

Computer Science
Information Sciences

Keywords

Disaster events Twitter situational information classification summarization.