Using Global Terrorism Database (GTD) and Machine Learning Algorithms to Predict Terrorism and Threat
S. Kalaiarasi1, Ankit Mehta2, Devyash Bordia3, Sanskar4

1S. Kalaiarasi*, Assistant Professor in SRM institute of Science and Technology and life Member of ISTE.
2Ankit Mehta, Bachelors in Science and Technology Degree in SRM.
3Devyash Bordia, Bachelors in Science and Technology Degree in SRM.
4Sanskar, Bachelors in Science and Technology Degree in SRM.
Manuscript received on September 10, 2019. | Revised Manuscript received on October 20, 2019. | Manuscript published on October 30, 2019. | PP: 5995-6000 | Volume-9 Issue-1, October 2019 | Retrieval Number: A1768109119/2019©BEIESP | DOI: 10.35940/ijeat.A1768.109119
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Abstract: It is evident that there has been enormous growth in terrorist attacks in recent years. The idea of online terrorism has also been growing its roots in the internet world. These types of activities have been growing along with the growth in internet technology. These types of events include social media threats such as hate speeches and comments provoking terror on social media platforms such as twitter, Facebook, etc. These activities must be prevented before it makes an impact. In this paper, we will make various classifiers that will group and predict various terrorism activities using k-NN algorithm and random forest algorithm. The purpose of this project is to use Global Terrorism Database as a dataset to detect terrorism. We will be using GTD which stands for Global Terrorism Database which is a publicly available database which contains information on terrorist event far and wide from 1970 through 2017 to train a machine learning-based intelligent system to predict any future events that could bring threat to the society.
Keywords: Data Mining Global Terrorism Database (GTD), Social Media Threats,