Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Conference Papers Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Categorizing Disaster Tweets Using Learning Based Models for Emergency Crisis Management Subject The topic of the resource Bi-LSTM; Deep Learning; Disaster Management; Disaster Tweets; Machine Learning; Natural Language Processing; Support Vector Machine; TF-IDF Vectorizer; Word2Vec Vectorizer Description An account of the resource Social media communication is essential to the crisis response aftermath of a massive tragedy. Facebook, Twitter, and other social media network platforms are effective instruments for connecting and fostering collaboration among catastrophe victims and other groups. As a result, numerous research publications on tweet analysis have been released. Tweet analysis during a crisis helps in understanding the nuances of the incident. Existing works primarily focused on tweet sentiment analysis and binary categorization of tweets into catastrophe relevant or not. Our work mainly categorizes catastrophe tweets into seven categories: Blizzard, earthquake, flood, hurricane, tornado, wildfire, and not-relevant tweets. Deep learning and machine learning methods were employed to categorize the tweets. The annotated data is subjected to classification using Support Vector Machine (SVM) utilizing Term Frequency-Inverse Document Frequency (TF-IDF) Vectorizer and Word2Vec Vectorizer and compares the accuracy of different kernel functions. Bidirectional Long Short Term Memory (Bi-LSTM) is used on the labeled data as a deep learning technique. SVM exhibited 88% accuracy compared to 87% for Bi-LSTM. Empirical evidence shows that our methodology is more productive and efficient than previous approaches. From this knowledge of the incident, emergency aid organizations may draw conclusions and act immediately. 2023 IEEE. Creator An entity primarily responsible for making the resource Asinthara K.; Jayan M.; Jacob L. Source A related resource from which the described resource is derived 2023 9th International Conference on Advanced Computing and Communication Systems, ICACCS 2023, pp. 1133-1138. Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Date A point or period of time associated with an event in the lifecycle of the resource 2023-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICACCS57279.2023.10113105" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICACCS57279.2023.10113105</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85159760505&amp;doi=10.1109%2FICACCS57279.2023.10113105&amp;partnerID=40&amp;md5=18858763556b708a0d2d910b5c1b9a46" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85159760505&amp;doi=10.1109%2fICACCS57279.2023.10113105&amp;partnerID=40&amp;md5=18858763556b708a0d2d910b5c1b9a46</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 979-835039737-6 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Conference paper Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Asinthara K., Christ University, Department of Data Science, Bangalore, India; Jayan M., Christ University, Department of Data Science, Bangalore, India; Jacob L., Christ University, Department of Data Science, Bangalore, India