Social networking platforms provide a vital source for disseminatinginformation across the globe, particularly in case of disaster. These platformsare great mean to find out the real account of the disaster. Twitter is an exampleof such platform, which has been extensively utilized by scientific community dueto its unidirectional model. It is considered a challenging task to identify eyewitness tweets about the incident from the millions of tweets shared by twitter users.Research community has proposed diverse sets of techniques to identify eyewitness account. A recent state-of-the-art approach has proposed a comprehensive setof features to identify eyewitness account. However, this approach suffers somelimitation. Firstly, automatically extracting the feature-words remains a perplexingtask against each feature identified by the approach. Secondly, all identified features were not incorporated in the implementation. This paper has utilized the language structure, linguistics, and word relation to achieve automatic extraction offeature-words by creating grammar rules. Additionally, all identified features wereimplemented which were left out by the state-of-the-art model. A genericapproach is taken to cover different types of disaster such as earthquakes, floods,hurricanes, and wildfires. The proposed approach was then evaluated for all disaster-types, including earthquakes, floods, hurricanes, and fire. Based on the staticdictionary, the Zahra et al. approach was able to produce an F-Score value of0.92 for Eyewitness identification in the earthquake category. The proposedapproach secured F-Score values of 0.81 in the same category. This score canbe considered as a significant score without using a static dictionary.
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