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.

本研究提出了一种新的半监督方法，利用推特数据和无监督异常检测算法识别车辆劫持事件，比较评估结果表明 Cluster Based Outlier Factor 方法在准确性和 F1-Score 上优于 K-Nearest Neighbour，因此 CBLOF 方法被选择作为更好的无监督方法，并计划在更大的数据集上使用监督学习方法进行比较和优化。