Tweets to Predict: LSTM Model for Crime Analysis Using Twitter Time Series Data

In recent years, social media platforms have emerged as rich sources of real-time data that can be leveraged for various predictive analytics tasks. This study explores the utility of Twitter data for crime analysis through the application of Long Short-Term Memory (LSTM) models. Specifically, we propose a novel LSTM-based approach for time series forecasting using Twitter data to predict crime occurrences. The methodology involves the collection of large-scale Twitter data streams relevant to crime incidents in urban areas. We preprocess the data to extract pertinent features and transform it into a suitable format for LSTM modeling. Through a series of experiments, we evaluate the performance of the LSTM model in forecasting crime trends based on historical Twitter time series data. Our results demonstrate the efficacy of the proposed LSTM-based approach in predicting crime occurrences with high accuracy and temporal precision. By analyzing Twitter signals, our model effectively captures subtle patterns and correlations underlying crime dynamics, enabling proactive measures for crime prevention and law enforcement.