Semi-Supervised Anomaly Detection for the Determination of Vehicle Hijacking Tweets

TL;DR

This paper introduces a semi-supervised approach using anomaly detection algorithms to identify vehicle hijacking incidents from tweets, achieving high accuracy and F1-scores, with CBLOF slightly outperforming KNN.

Contribution

The work presents a novel semi-supervised method combining TF-IDF with anomaly detection algorithms for hijacking tweet detection, demonstrating effectiveness over traditional approaches.

Findings

CBLOF achieved 90% accuracy and 0.8 F1-score

KNN achieved 89% accuracy and 0.78 F1-score

CBLOF was identified as the preferred method

Abstract

In South Africa, there is an ever-growing issue of vehicle hijackings. This leads to travellers constantly being in fear of becoming a victim to such an incident. This work presents a new semi-supervised approach to using tweets to identify hijacking incidents by using unsupervised anomaly detection algorithms. Tweets consisting of the keyword "hijacking" are obtained, stored, and processed using the term frequency-inverse document frequency (TF-IDF) and further analyzed by using two anomaly detection algorithms: 1) K-Nearest Neighbour (KNN); 2) Cluster Based Outlier Factor (CBLOF). The comparative evaluation showed that the KNN method produced an accuracy of 89%, whereas the CBLOF produced an accuracy of 90%. The CBLOF method was also able to obtain a F1-Score of 0.8, whereas the KNN produced a 0.78. Therefore, there is a slight difference between the two approaches, in favour of CBLOF, which has been selected as a preferred unsupervised method for the determination of relevant hijacking tweets. In future, a comparison will be done between supervised learning methods and the unsupervised methods presented in this work on larger dataset. Optimisation mechanisms will also be employed in order to increase the overall performance.