期刊名称：Journal of Theoretical and Applied Information Technology
印刷版ISSN：1992-8645
电子版ISSN：1817-3195
出版年度：2018
卷号：96
期号：8
出版社：Journal of Theoretical and Applied
摘要：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 data-set. Experiments also revealed that the proposed system achieves accuracy that outperforms the system proposed by Hend.et.al [29] and TweetCred [2].
关键词：News Credibility; Arabic News; Machine Learning; Twitter; Verifiability; Text Polarity
