Earticle

초록

영어

The advent of social computing brought with it different social networking platforms. The idea of surfers socializing with people of different backgrounds as well as geographical regions is quite fascinating. In our approach, we delved deeper in disaster discovery whereby we extracted panic related attributes and trained them with real data in three disaster scenarios in different parts of the world. Fine tuning of the final attributes led to accuracies above 91% proving the fact that with proper attribute selection and handling of sparse data balance, it’s possible to detect related disasters as soon as related tweets appear. We believe that we are the first to use probabilistic classifiers approach as well as NLP in specifically human induced terror attacks detection as there is no known system currently that solely caters for these.

목차

Abstract
 1. Introduction
 2. Related Works
 3. Our Approach
  3.1 Methodology
  3.2 Data Sampling and Preprocessing
  3.3 Model Construction and Evaluation
  3.4 Evaluation Criteria and Findings
  3.5 Class Imbalance, Information Gain and Ranking
 4. Conclusion
 5. Future Work
 References

키워드

저자

• Herman Wandabwa [ School of Information and Communication Engineering, Central South University, Changsha, China ]
• Liao Zhifang [ School of Software, Central South University, Changsha, China ]
• Korir Sammy [ School of Information and Communication Engineering, Central South University, Changsha, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Database Theory and Application

• 간기

  격월간

• pISSN

  2005-4270

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605