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 Articles Article Faculty Publications -Articles 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 Spatio-temporal Crime Analysis and Forecasting on Twitter Data Using Machine LearningAlgorithms Subject The topic of the resource Crime analysis; Crime prevention; Forecasting; Machine learning algorithms; Twitter data Description An account of the resource The concept of social media began to gain popularity in the late 1990s and has played a significant role in connecting people across the globe. The constant addition of features to old social media platforms and the creation of new ones have helped amass and retain an extensive user base. Users could now share their views and provide detailed accounts of events from worldwide to reach like-minded people. This led to the popularization of blogging and brought into focus the posts of the commoner. These posts began to be verified and included in mainstream news articles bringing about a revolution in journalism. This research aims to use a social media platform, Twitter, to classify, visualize, and forecast Indian crime tweet data and provide a spatio-temporal view of crime in the country using statistical and machine learning models. The Tweepy Python module's search function and '#crime' query have been used to scrape relevant tweets under geographical constraints, followed by substring-keyword classification using 318 unique crime keywords. The Bokeh and gmaps Python modules create analytical and geospatial visualizations, respectively. Time series forecasting of crime tweet count is performed by comparing the accuracy of Long Short-Term Memory (LSTM), Auto-Regressive Integrated Moving Average (ARIMA), and Seasonal Auto-Regressivee Integrated Moving Average (SARIMA) models to determine the best model. 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. Creator An entity primarily responsible for making the resource Vivek M.; Prathap B.R. Source A related resource from which the described resource is derived SN Computer Science, Vol-4, No. 4 Publisher An entity responsible for making the resource available Springer 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/10.1007/s42979-023-01816-y" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/s42979-023-01816-y</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85159938122&amp;doi=10.1007%2Fs42979-023-01816-y&amp;partnerID=40&amp;md5=62444baa05559f8a98f33011388f0c13" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85159938122&amp;doi=10.1007%2fs42979-023-01816-y&amp;partnerID=40&amp;md5=62444baa05559f8a98f33011388f0c13</a> Rights Information about rights held in and over the resource All Open Access; Bronze Open Access; Green Open Access Relation A related resource ISSN: 2662995X 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 Article Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Vivek M., Computer Science and Engineering, CHRIST (Deemed to be University), Bangalore, India; Prathap B.R., Computer Science and Engineering, CHRIST (Deemed to be University), Bangalore, India