Comparative study of statistical features to detect the target event during disaster

Microblogs, such as facebook and twitter, have much attention among the users and organizations. Nowadays, twitter is more popular because of its real-time nature. People often interacted with real-time events such as earthquakes and floods through twitter. During a disaster, the number of posts or tweets is drastically increased in twitter. At the time of the disaster, detecting a target event is a challenging task. In this paper, a framework is proposed for observing the tweets and to detect the target event. For detecting the target event, a classifier is devised based on different combinations of statistical features such as the position of the keyword in a tweet, length of a tweet, the frequency of hashtag, and frequency of user mentions and the URL. From the result, it is evident that the combination of frequency of hashtag and position of keyword features provides good classification results than the other combinations of features. Hence, usage of two features, namely, frequency of hashtag and position of the earthquake keyword reduces the event's detection time. And also these two features are further helpful for detecting the sub-events which are used for filtering the tweets related to the disaster. Additionally, different classifiers such as Artificial Neural Networks (ANN), decision tree, and K-Nearest Neighbor (KNN) are compared by using these two features. However, Support Vector Machine (SVM) with linear kernel by using the combination of position of earthquake keyword and frequency of hashtag outperforms state-of-the-art methods. Therefore, SVM (linear kernel) with proposed features is applied for detecting the earthquake during disaster. The proposed algorithm is tested on Nepal earthquake and landslide datasets, 2015.