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A Framework for Analyzing Real-Time Tweets to Detect Terrorist Activities

Authors :
Md. Sabir Hossain
Mohammad Fahim Abrar
Mohammad Shamsul Arefin
Source :
2019 International Conference on Electrical, Computer and Communication Engineering (ECCE).
Publication Year :
2019
Publisher :
IEEE, 2019.

Abstract

Terrorist organizations use different social media as a tool for spreading their views and influence general people to join their terrorist activities. Twitter is the most common and easy way to reach mass people within a small amount of time. In this paper, we have focused on the development of a system that can automatically detect terrorism-supporting tweets by real-time analyzation. In this system, we have developed a frontend for realtime viewing of the tweets that are detected using this system. We have also compared the performance of two different machine learning classifiers, Support Vector Machine (SVM) and Multinomial Logistic Regression and foundthe first one works better. As our system is highly dependent on data, for more accuracy we added a re-train module. By using this module wrongly classified tweets can be added to the training dataset and train the whole system again for better performance. This system will help to ban the terrorist accounts from twitter so that they can't promote their views or spread fear among general people.

Details

Database :
OpenAIRE
Journal :
2019 International Conference on Electrical, Computer and Communication Engineering (ECCE)
Accession number :
edsair.doi...........0f2d07c8034b70108021a246ef2df4ca
Full Text :
https://doi.org/10.1109/ecace.2019.8679430