Automatic Twitter Crime Prediction Using Hybrid Wavelet Convolutional Neural Network with World Cup Optimization

Abstract

Social media are digitally mediated platforms that allow people to create and exchange content, professional interests, ideas, and other forms of expression through virtual networks. Users often utilize web-based programs on their PCs and laptops to visit social media sites, or they download programs that provide their devices social media capabilities. As users connect with these platforms, groups, organizations, and individuals can upload, co-create, discuss, engage in, and update self-curated or user-generated information. Although the platforms such as Facebook, Twitter, Instagram, etc., aid in the communication purposes, it also has some demerits like cyber-crime, hacking, etc. The growing number of crimes through these platforms needs to be deducted by predicting the crimes. For the crime prediction, the data acquired from Twitter is pre-processed for the data cleansing process. Later the features are extracted using various techniques like bag of words (BoW), Glove, term frequency-inverse document frequency (TF-IDF), and feature hashing. The feature selection is done using a modified tree growth algorithm (MTGA) and clustering is performed using the fuzzy manta ray foraging (FMRF). Finally, the crime detection is done using hybrid wavelet convolutional neural network with world cup organization (WCNN-WCO). The PYTHON tool is used for the implementation and the Twitter user dataset is used for analysis. The results showed that the proposed method outperforms the existing method in terms of precision, accuracy, [Formula: see text]1 measure, and recall.