원문정보
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
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