A Machine Learning Based Framework for Collecting and Using Social Media for Real-time Terrorist Attacks Prediction

Abstract

Terrorism has become a global plague causing insecurity and jeopardizing the development of many countries. In the past few years, terrorism has exploded in Burkina Faso, affecting education, national security, health, and the economy. There is a great need for solutions to detect and stop terrorist attacks before they occur. This research project seeks to use Artificial Intelligence (AI) to mine social media and detect probable future terrorist attacks. This article describes the design of a framework, its partial implementation, and an experiment to validate the technique. The system consists of five steps: taking social media as input, converting it to text, validating it, extracting essential information, predicting its class, and storing it in a dataset. The modest size of the manually produced dataset utilized in the original experiment is a key drawback of the work discussed in this research. The modest size proved inconvenient for Deep Learning algorithms, which operate best with massive datasets. When we complete the entire system, inserting increased data from social media into the dataset will resolve this limitation. The other limitation is the partial implementation of the framework, which does not provide a comprehensive picture of the proposed approach. Our future works will address the remainder parts of the proposed framework.

https://doi.org/10.59200/ICARTI.2023.013