Adaptable Reduced-Complexity Approach Based on State Vector Machine for Identification of Criminal Activists on Social Media

Abstract

Security agencies face an emerging challenge of identifying and counter the malicious contents spread on the social media by the terrorists. However, text classification techniques are limited by visualization, pre-processing, features extraction, and larger features space. Additionally, change in criminal content require the learning models to identify altered malicious textual contents which poses extra challenge. This study proposes simplified yet adaptable framework that uses a novel features extraction algorithm for extracting features from the textual part of social media contents. The feature extraction considers selective features from only 8 dimensions and follows a six step process. The extracted features are suitably used to train the state vector machine for the classification of the malicious content. The performance of the proposed method is evaluated against other popular feature selection/extraction algorithms like term frequency-inverse document frequency, Gini Index (GI), Chi square statistics, and PCA. Additionally, machine learning classifiers like decision tree, random forest, and Naïve Bayes are also used for classification. Results suggest that the proposed approach consumes less energy on text visualization, pre-processing, and dimensionality reduction. It also reduces the time-space complexity of the features extraction process and is capable to steer according to the changing strategies of the active criminal groups. In addition, it can effectively analyze the propaganda material published by the extremists. It automatically identifies the radical text on social media platforms allowing understanding of the behaviors, characteristics and subsequent blockage of such content.