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Dr. Sahar Fawzy Abdel Razek :: Publications:

Arabic news credibility on Twitter: An Enhanced Model using Hybrid Features
Authors: 4. Sahar F. Sabbeh, Sumaiah Y. Baatwah
Year: 2018
Keywords: Not Available
Journal: Journal of Theoretical and Applied Information Technology
Volume: 96
Issue: Not Available
Pages: Not Available
Publisher: Not Available
Local/International: International
Paper Link: Not Available
Full paper Not Available
Supplementary materials Not Available

Recently, social media and specially Twitter has become a main source for news consumption and sharing among millions of users. Those platforms enable users to author, publish and share content. Such environments can be used to publish and spread rumors and fake news whether unintentionally or even maliciously. That is why credibility of information in such platforms has been increasingly investigated in many domains (i.e. information sciences, psychology, sociology...etc). This paper proposes a machine learning - based model for Arabic news credibility assessment on Twitter. It uses hybrid set of features that are topic and user related to evaluate news credibility. In addition to the traditional content-related features, Content verifiability and users' replies polarity analysis used for a more accurate assessment. The proposed model consists of four main modules: a) content parsing and features extraction module, b) content verification module, c) users’ comments polarity evaluation and d) credibility classification module. A data set of 800 Arabic news that are manually labeled is collected from Twitter. Three different classification techniques were applied (Decision tree, support vector machine (SVM) and Naive Bayesian(NB). For model training and testing, 5-fold cross validations were performed and performance diagnostics were calculated. Results indicate that decision tree achieves TRP higher than SVM by around 2% and 7% than NB, also FPR almost 9% lower than SVM and 10% lower than NB. For precision,recall, f-measure and accuracy, decision tree achieves almost 2% higher than SVM and 7% higher than NB for the tested dataset. Experiments also revealed that the proposed system achieves accuracy that outperforms the system proposed by [29] and TweetCred [2].

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