Mohamed Taha

Work place: Benha University, Faculty of Computers and Artificial intelligence, Computer Science Department, Benha,13518, Egypt



Research Interests: Computer Vision


Mohamed Taha is an Assistant Professor at Benha University, Faculty of Computers and Artificial intelligence, Computer Science Department, Egypt. He received his M.Sc. degree and his Ph.D. degree in computer science at Ain Shams University, Egypt, in February 2009 and July 2015. His research interest's concern: Computer Vision (Object Tracking-Video Surveillance Systems), Digital Forensics (Image Forgery Detection – Document Forgery Detection - Fake Currency Detection), Image Processing (OCR), Computer Network (Routing Protocols - Security), Augmented Reality, Cloud Computing, and Data Mining (Association Rules Mining-Knowledge Discovery). Taha has contributed more than 20+ technical papers to international journals and conferences.

Author Articles
Credibility Detection on Twitter News Using Machine Learning Approach

By Marina Azer Mohamed Taha Hala H. Zayed Mahmoud Gadallah

DOI:, Pub. Date: 8 Jun. 2021

Social media presence is a crucial portion of our life. It is considered one of the most important sources of information than traditional sources. Twitter has become one of the prevalent social sites for exchanging viewpoints and feelings. This work proposes a supervised machine learning system for discovering false news. One of the credibility detection problems is finding new features that are most predictive to better performance classifiers. Both features depending on new content, and features based on the user are used. The features' importance is examined, and their impact on the performance. The reasons for choosing the final feature set using the k-best method are explained. Seven supervised machine learning classifiers are used. They are Naïve Bayes (NB), Support vector machine (SVM), K-nearest neighbors (KNN), Logistic Regression (LR), Random Forest (RF), Maximum entropy (ME), and conditional random forest (CRF). Training and testing models were conducted using the Pheme dataset. The feature's analysis is introduced and compared to the features depending on the content, as the decisive factors in determining the validity. Random forest shows the highest performance while using user-based features only and using a mixture of both types of features; features depending on content and the features based on the user, accuracy (82.2 %) in using user-based features only. We achieved the highest results by using both types of features, utilizing random forest classifier accuracy(83.4%). In contrast, logistic regression was the best as to using features that are based on contents. Performance is measured by different measurements accuracy, precision, recall, and F1_score. We compared our feature set with other studies' features and the impact of our new features. We found that our conclusions exhibit high enhancement concerning discovering and verifying the false news regarding the discovery and verification of false news, comparing it to the current results of how it is developed.

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