The <span>digital social platform is an important medium for sharing or communicating a message from one person to another or one to many. The growth of internet users and social media use has also led to many adverse consequences. Such a platform is also used for radical activity by spreading the radical message in public. The detection of such a message is impossible by human monitoring. Many researchers are continually working on automatic detection of such activity to find a way to stop it. Automatic identification is also not possible due to the massive amount of data present and ambiguity in messages. The proposed work presents a framework for detecting the radical message and taking action by automatically blocking it. A dataset of 33k tweets has been fetched from twitter based on radical words. Two machine learning models, first countervectorizer and Logistic regression-based and second convolutional neural networks (CNN) have been applied yielding 96.97% accuracy. The provision of human intervention is also given in doubt cases which helps further to improve the accuracy of overall model. The framework gives very good results in a simulated environment</span>.
The social media in digital form over internet is getting popularity in recent years. This digital platform is being used by many to share their thought or opinion. Though these social media had given results too many good causes, there are some users are present on these platforms for radical activities. In this paper, the tweets form the digital social platform twitter is taken for analysis based on radical keywords. The data is collected in form of tweets are analyzed using different machine learning algorithms and a comparative analysis is done. The proposed work concludes the best machine learning algorithms for analysis of such data and the new words came in light for the collected dataset. The deep learning model are also implemented and tested for sentimental analysis.
HIGHLIGHTS
Radical keywords-based message collection from social platforms
Application of various machine learning and deep learning algorithms trained using collected datasets
Discovery of new temporal words
Identification of radical messages floating on social platforms
Identifying the best performing machine learning and deep learning algorithm for radical message analysis
GRAPHICAL ABSTRACT
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