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A Framework for Analyzing Real-Time Tweets to Detect Terrorist Activities
- 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.
- Subjects :
- business.industry
Computer science
020206 networking & telecommunications
02 engineering and technology
Machine learning
computer.software_genre
Whole systems
Support vector machine
Terrorism
0202 electrical engineering, electronic engineering, information engineering
020201 artificial intelligence & image processing
Social media
Artificial intelligence
business
computer
Multinomial logistic regression
Subjects
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