Analyzing Newspaper Articles for Text-Related Data for Finding Vulnerable Posts Over the Internet That Are Linked to Terrorist Activities

Romil Rawat, Vinod Mahor, Bhagwati Garg, Shrikant Telang, Kiran Pachlasiya, Anil Kumar, Surendra Kumar Shukla and Megha Kuliha
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Romil Rawat: Shri Vaishnav Vidyapeeth Vishwavidyalaya, India
Vinod Mahor: IPS College of Technology and Management, India
Bhagwati Garg: Union Bank of India, India
Shrikant Telang: Shri Vaishnav Vidyapeeth Vishwavidyalaya, India
Kiran Pachlasiya: NRI Institute of Science and Technology, India
Anil Kumar: Government Engineering College, Bharatpur, India
Surendra Kumar Shukla: Graphic Era Deemed to be University, Deharadun, India
Megha Kuliha: Shri G.S. Institute of Technology and Science, Indore, India

International Journal of Information Security and Privacy (IJISP), 2022, vol. 16, issue 1, 1-14

Abstract: One of the most critical activities of revealing terrorism-related information is classifying online documents.The internet provides consumers with a variety of useful knowledge, and the volume of web material is increasingly growing. This makes finding potentially hazardous records incredibly difficult. To define the contents, merely extracting keywords from records is inadequate. Many methods have been studied so far to develop automatic document classification systems, they are mainly computational and knowledge-based approaches. due to the complexities of natural languages, these approaches do not provide sufficient results. To fix this shortcoming, we given approach of structure dependent on the WordNet hierarchy and the frequency of n-gram data that employs word similarity. Using four different queries terms from four different regions, this approach was checked for the NY Times articles that were sampled. Our suggested approach successfully removes background words and phrases from the document recognizes connected to terrorism texts, according to experimental findings.

Date: 2022
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International Journal of Information Security and Privacy (IJISP) is currently edited by Yassine Maleh

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