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Title |
Real Time Arabic Communities Attack Detection on Online Social Networks |
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Author |
Jalal S Alowibdi |
| Citation |
Vol. 24 No. 8 pp. 61-71 |
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Abstract |
The dynamic nature of Online Social Networks (OSNs), especially on platforms like Twitter, presents challenges in identifying and responding to community attacks, particularly within Arabic content. The proposed integrated system addresses these challenges by achieving 91% accuracy in detecting real-time community event attacks while efficiently managing computational costs. This is accomplished through the use of specialized integrated approach clustering to detect both major and minor attacks. Additionally, the system leverages clustering algorithms, temporal modules, and social network graphs to identify events, map communities, and analyze online dynamics. An extensive parameter sensitivity analysis was conducted to fine-tune the algorithm, and the system's effectiveness was validated using a benchmark dataset, demonstrating substantial improvements in event detection. |
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Keywords |
. Real-time Event Detection, Community Attacks, Inverted Indices, Incremental Clustering, Arabic-speaking Community |
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