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    CAT: Credibility Analysis of Arabic Content on Twitter

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    Date
    2017
    Author
    El Ballouli, Rim
    El-Hajj, Wassim
    Ghandour, Ahmad
    Elbassuoni, Shady
    Hajj, Hazem
    Shaban, Khaled
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    Abstract
    Data generated on Twitter has become a rich source for various data mining tasks. Those data analysis tasks that are dependent on the tweet semantics, such as sentiment analysis, emotion mining, and rumor detection among others, suffer considerably if the tweet is not credible, not real, or spam. In this paper, we perform an extensive analysis on credibility of Arabic content on Twitter. We also build a classification model (CAT) to automatically predict the credibility of a given Arabic tweet. Of particular originality is the inclusion of features extracted directly or indirectly from the author's profile and timeline. To train and test CAT, we annotated for credibility a data set of 9, 000 Arabic tweets that are topic independent. CAT achieved consistent improvements in predicting the credibility of the tweets when compared to several baselines and when compared to the state-of-the-art approach with an improvement of 21% in weighted average F-measure. We also conducted experiments to highlight the importance of the user-based features as opposed to the content-based features. We conclude our work with a feature reduction experiment that highlights the best indicative features of credibility. 2017 Association for Computational Linguistics
    DOI/handle
    http://dx.doi.org/10.18653/v1/W17-1308
    http://hdl.handle.net/10576/37493
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    • Computer Science & Engineering [‎2428‎ items ]

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