A Twitter-Driven Deep Learning Mechanism for the Determination of Vehicle Hijacking Spots in Cities

TL;DR

This paper develops a deep learning-based system using Twitter data to identify and map vehicle hijacking hotspots in Cape Town, achieving high accuracy with CNN and aiming to assist public safety.

Contribution

It introduces a novel approach combining Twitter data and deep learning models, especially CNN, to detect hijacking reports and visualize them on a city map.

Findings

CNN achieved 99.66% accuracy in identifying relevant tweets

The system effectively visualizes hijacking hotspots in Cape Town

Deep learning models can assist in real-time crime monitoring

Abstract

Vehicle hijacking is one of the leading crimes in many cities. For instance, in South Africa, drivers must constantly remain vigilant on the road in order to ensure that they do not become hijacking victims. This work is aimed at developing a map depicting hijacking spots in a city by using Twitter data. Tweets, which include the keyword "hijacking", are obtained in a designated city of Cape Town, in this work. In order to extract relevant tweets, these tweets are analyzed by using the following machine learning techniques: 1) a Multi-layer Feed-forward Neural Network (MLFNN); 2) Convolutional Neural Network; and Bidirectional Encoder Representations from Transformers (BERT). Through training and testing, CNN achieved an accuracy of 99.66%, while MLFNN and BERT achieve accuracies of 98.99% and 73.99% respectively. In terms of Recall, Precision and F1-score, CNN also achieved the best results. Therefore, CNN was used for the identification of relevant tweets. The relevant reports that it generates are visually presented on a points map of the City of Cape Town. This work used a small dataset of 426 tweets. In future, the use of evolutionary computation will be explored for purposes of optimizing the deep learning models. A mobile application is under development to make this information usable by the general public.