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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), Knearest 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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The availability of a machine translation to translate from English-to-Arabic with high accuracy is not available because of the difficult morphology of the Arabic Language. A hybrid machine translation system between Example Based machine translation technique and Translation memory was introduced in this paper. Two datasets have been used in the experiments that were constructed by using internal medicine publications and Worldwide Arabic Medical Translation Guide Common Medical Terms sorted by Arabic. To examine the accuracy of the system constructed four experiments were made using Example Based Machine Translation system in the first, Google Translate in the second and Example Based with Google translate in the third and the fourth is the system proposed using Example Based with Translation memory. The system constructed achieved 77.17 score for the first dataset and 63.85 score for the second which were the highest score using BLEU score.

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