Abstract The ability to post short text and media messages on Social media platforms like Twitter, Facebook, etc., plays a huge role in the exchange of information following a mass emergency event like hurricane, earthquake, tsunami etc. Disaster victims, families, and other relief operation teams utilize social media to help and support one another. Despite the benefits offered by these communication media, the disaster topic related posts (posts that indicate conversations about the disaster event in the aftermath of the disaster) gets lost in the deluge of posts since there would be a surge in the amount of data that gets exchanged following a mass emergency event. This hampers the emergency relief effort, which in turn affects the delivery of useful information to the disaster victims. Research in emergency coordination via social media has received growing interest in recent years, mainly focusing on developing machine learning-based models that can separate disaster-related topic posts from non- disaster related topic posts. Of these, supervised machine learning approaches performed well when the machine learning model trained using source disaster dataset and target disaster dataset are similar. However, in the real world, it may not be feasible as different disasters have different characteristics. So, models developed using supervised machine learning approaches do not perform well in unseen disaster datasets. Therefore, domain adaptation approaches, which address the above limitation by learning classifiers from unlabeled target data in addition to source labelled data, represent a promising direction for social media crisis data classification tasks. The existing domain adaptation techniques for the classification of disaster tweets are experimented with using single disaster event dataset pairs; then, self-training is performed on the source target dataset pairs by considering the highly confident instances in subsequent iterations of training. This could be improved with better feature engineering. Thus, this research proposes a Genetic Algorithm based Domain Adaptation Framework (GADA) for the classification of disaster tweets. The proposed GADA combines the power of 1) Hybrid Feature Selection component using the Genetic Algorithm and Chi-Square Feature Evaluator for feature selection and 2) the Classifier component using Random Forest to classify disaster-related posts from noise on Twitter. The proposed framework addresses the challenge of the lack of labeled data in the target disaster event by proposing a Genetic Algorithm based approach. Experimental results on Twitter datasets corresponding to four disaster domain pair shows that the proposed framework improves the overall performance of the previous supervised approaches and significantly reduces the training time over the previous domain adaptation techniques that do not use the Genetic Algorithm (GA) for feature selection.

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[30] Zhou Y., De S., and Moessner K., “Real World City Event Extraction from Twitter Data Streams,” Procedia Computer Science, pp. 443-448, 2016. Lokabhiram Dwarakanath received the B.Engg degree in Electronics and Communication Engineering from Dr.MGR Engg College, University of Madras, India, and the M.Sc. degree in Enterprise Business Systems from the Brunel University, West London, U.K. He is currently pursuing the Ph.D. degree in the Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur, Malaysia. His research interests include data science, natural language processing, information systems, cloud computing, machine learning, big data, and social media analytics. Amirrudin Kamsin is a Senior Lecturer at the Faculty of Computer Science and Information Technology, and the Acting Director and Deputy Director (ODL and Professional Programme) at the University of Malaya Centre for Continuing Education (UMCCed), University of Malaya, Malaysia. He received his BIT (Management) in 2001 and MSc in Computer Animation in 2002 from University of Malaya and Bournemouth University, UK respectively. He obtained his PhD in Computer Science from University College London (UCL) in 2014. His research areas include human-computer interaction (HCI), authentication systems, e-learning, mobile applications, serious game, augmented reality and mobile health services. Liyana Shuib obtained her Master of Information System (Data Mining) from Universiti Kebangsaan Malaysia in 2005 and a Ph.D. from the University of Malaya, Malaysia in 2013 respectively. She is an Associate Professor at the Department of Information Systems, Faculty of Computer Science & Information Technology and the Deputy Director of Analytics at Academic Strategic Planning Centre, Deputy Vice Chancellor (Academic & International), University of Malaya, Malaysia. She has published a number of journal papers and proceedings locally and internationally. Her research interests include personalization, e-learning, recommender system, data science, data mining, artificial intelligence application, and educational technology. She has won more than 20 awards from reputable innovation competition internationally. She is also a senior member of IEEE computing society, an active blogger and presently, the principal investigator of multiple research grant in the Faculty.