Categorizing temporal events: A case study of domestic terrorism

In many emergency incidents, multiple reports and information sources are often used to help intelligence and security personnel to understand the situation during a short time period. Proper categorization and analysis of this information could enhance the efficiency of handling this large amount of potentially conflicting information, thus contributing to saving lives. The study of categorization of temporal events in cyber security application is, however, not widely found. In this research, we developed an automated approach to categorizing temporal events described in textual documents. The approach consists of automatic indexing, term extraction, and automatic categorization. We conducted a case study of domestic terrorism where we analyzed 96 online news articles about a shooting tragedy that resulted in 6 deaths and 1 seriously injured. Analyses of different numbers of extracted textual features (from 20 to 100) used in the temporal categorization revealed a gradual improvement of classification accuracies across different algorithms used. Naïve Bayes and SVM classification provided stable improvement (from 47% to 68%), whereas Neural Network had the highest accuracy when 70 features were used. The results provide new insights for researchers and intelligence personnel to understand the relationship between textual features and emergency event evolution.