Geospatial and Forecasting on Twitter Crime-A Review of Research

The idea of social media started to gain popularity in the late 1990s and has been vital in bringing people together throughout the world. This study examines how social media, specifically Twitter, Facebook, and Instagram, can increase women's safety in Indian cities. This review article intends to categorize, visualize, and forecast Indian crime tweet data using the social media network Twitter and to present a spatiotemporal view of crime in the nation using statistical and machine learning algorithms. The relevant tweets were retrieved using the search feature of the Tweepy Python package and the ‘#crime’ query, which was then classified using 318 distinct criminal keywords. Geospatial and analytical visualizations are produced by the Python modules gmaps and bokeh, respectively. The accuracy of the Long Short-Term Memory (LSTM), Auto-Regressive Integrated Moving Average (ARIMA), and Seasonal Auto-Regressive Integrated Moving Average (SARIMA) models are compared in order to find the best model for time series forecasting of crime tweet count.