Machine Learning to Classify Religious Communities and Detect Extremism on Social Networks

Religion is a source of mercy and peace; religious texts are one of the most critical parts of a culture's heritage, and they affect societies often in a big way; sadly, misconceptions can also make some religious people extremists. Modern social networks provide a platform for people to express themselves share their opinions and show their affiliations on many topics. This generates data in many forms like photos, videos, and texts. The authors used predefined machine learning (ML) to classify and analyze textual data from social networks. In this paper, they focus on two types of classification: religious and extremist. Extremism is independent of religious text, and therefore, they classify them separately. The work uses and compares several algorithms to classify textual data from social networks. The proposed model has achieved 93.33% accuracy for religious classification and 97% accuracy for extremism detection.

• social networks
• religious texts
• text classification
• algorithm comparison
• online religious discourse

• Text and Document Classification Technologies
• Hate Speech and Cyberbullying Detection
• Terrorism, Counterterrorism, and Political Violence
• social networks
• religious texts
• text classification
• algorithm comparison
• online religious discourse
• Artificial Intelligence
• Sociology and Political Science
• Computer Science
• Social Sciences