Open Access

ARTICLE

Automatic Eyewitness Identification During Disasters by Forming a Feature-Word Dictionary

by Shahzad Nazir¹, Muhammad Asif^1,*, Shahbaz Ahmad¹, Hanan Aljuaid², Shahbaz Ahmad¹, Yazeed Ghadi³, Zubair nawaz⁴

1 Department of Computer Science, National Textile University, Faisalabad, Pakistan
2 Computer Sciences Department, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University (PNU), P.O. Box 84428, Riyadh 11671, Saudi Arabia
3 Department of Software Engineering and Computer Science, Al Ain University, Abu Dhabi, United Arab Emirates
4 Department of Data Science, University of the Punjab, Pakistan

* Corresponding Author: Muhammad Asif. Email:

Computers, Materials & Continua 2022, 72(3), 4755-4769. https://doi.org/10.32604/cmc.2022.026145

Received 16 December 2021; Accepted 08 March 2022; Issue published 21 April 2022

Abstract

Social media provide digitally interactional technologies to facilitate information sharing and exchanging individuals. Precisely, in case of disasters, a massive corpus is placed on platforms such as Twitter. Eyewitness accounts can benefit humanitarian organizations and agencies, but identifying the eyewitness Tweets related to the disaster from millions of Tweets is difficult. Different approaches have been developed to address this kind of problem. The recent state-of-the-art system was based on a manually created dictionary and this approach was further refined by introducing linguistic rules. However, these approaches suffer from limitations as they are dataset-dependent and not scalable. In this paper, we proposed a method to identify eyewitnesses from Twitter. To experiment, we utilized 13 features discovered by the pioneer of this domain and can classify the tweets to determine the eyewitness. Considering each feature, a dictionary of words was created with the Word Dictionary Maker algorithm, which is the crucial contribution of this research. This algorithm inputs some terms relevant to a specific feature for its initialization and then creates the words dictionary. Further, keyword matching for each feature in tweets is performed. If a feature exists in a tweet, it is termed as 1; otherwise, 0. Similarly, for 13 features, we created a file that reflects features in each tweet. To classify the tweets based on features, Naïve Bayes, Random Forest, and Neural Network were utilized. The approach was implemented on different disasters like earthquakes, floods, hurricanes, and Forest fires. The results were compared with the state-of-the-art linguistic rule-based system with 0.81 F-measure values. At the same time, the proposed approach gained a 0.88 value of F-measure. The results were comparable as the proposed approach is not dataset-dependent. Therefore, it can be used for the identification of eyewitness accounts.

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Keywords

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