Open Access

ARTICLE

Autonomous Eyewitness Identification by Employing Linguistic Rules for Disaster Events

by Sajjad Haider^*, Muhammad Tanvir Afzal

Capital University of Science and Technology, Islamabad, 44000, Pakistan

* Corresponding Author: Sajjad Haider. Email:

Computers, Materials & Continua 2021, 66(1), 481-498. https://doi.org/10.32604/cmc.2020.012057

Received 12 June 2020; Accepted 03 August 2020; Issue published 30 October 2020

Abstract

Social networking platforms provide a vital source for disseminating information across the globe, particularly in case of disaster. These platforms are great mean to find out the real account of the disaster. Twitter is an example of such platform, which has been extensively utilized by scientific community due to 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 set of features to identify eyewitness account. However, this approach suffers some limitation. Firstly, automatically extracting the feature-words remains a perplexing task 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 of feature-words by creating grammar rules. Additionally, all identified features were implemented which were left out by the state-of-the-art model. A generic approach 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 static dictionary, the Zahra et al. approach was able to produce an F-Score value of 0.92 for Eyewitness identification in the earthquake category. The proposed approach secured F-Score values of 0.81 in the same category. This score can be considered as a significant score without using a static dictionary.

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