Arabic corpora for credibility analysis

Ayman Al Zaatari, Rim El Ballouli, Shady Elbassuoni, Wassim El-Hajj, Hazem Hajj, Khaled Shaban, Nizar Habash, Emad Yehya

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Abstract

    A significant portion of data generated on blogging and microblogging websites is non-credible as shown in many recent studies. To filter out such non-credible information, machine learning can be deployed to build automatic credibility classifiers. However, as in the case with most supervised machine learning approaches, a sufficiently large and accurate training data must be available. In this paper, we focus on building a public Arabic corpus of blogs and microblogs that can be used for credibility classification. We focus on Arabic due to the recent popularity of blogs and microblogs in the Arab World and due to the lack of any such public corpora in Arabic. We discuss our data acquisition approach and annotation process, provide rigid analysis on the annotated data and finally report some results on the effectiveness of our data for credibility classification.

    Original languageEnglish (US)
    Title of host publicationProceedings of the 10th International Conference on Language Resources and Evaluation, LREC 2016
    PublisherEuropean Language Resources Association (ELRA)
    Pages4396-4401
    Number of pages6
    ISBN (Electronic)9782951740891
    StatePublished - Jan 1 2016
    Event10th International Conference on Language Resources and Evaluation, LREC 2016 - Portoroz, Slovenia
    Duration: May 23 2016May 28 2016

    Other

    Other10th International Conference on Language Resources and Evaluation, LREC 2016
    CountrySlovenia
    CityPortoroz
    Period5/23/165/28/16

    Fingerprint

    credibility
    weblog
    data acquisition
    learning
    popularity
    Arab
    website
    Credibility
    lack
    Blogs
    Machine Learning
    Web Sites
    Arab World
    Filter
    Blogging
    Classifier
    Annotation

    Keywords

    • Blogs
    • Credibility
    • Crowdsourcing
    • Twitter

    ASJC Scopus subject areas

    • Linguistics and Language
    • Library and Information Sciences
    • Language and Linguistics
    • Education

    Cite this

    Al Zaatari, A., El Ballouli, R., Elbassuoni, S., El-Hajj, W., Hajj, H., Shaban, K., ... Yehya, E. (2016). Arabic corpora for credibility analysis. In Proceedings of the 10th International Conference on Language Resources and Evaluation, LREC 2016 (pp. 4396-4401). European Language Resources Association (ELRA).

    Arabic corpora for credibility analysis. / Al Zaatari, Ayman; El Ballouli, Rim; Elbassuoni, Shady; El-Hajj, Wassim; Hajj, Hazem; Shaban, Khaled; Habash, Nizar; Yehya, Emad.

    Proceedings of the 10th International Conference on Language Resources and Evaluation, LREC 2016. European Language Resources Association (ELRA), 2016. p. 4396-4401.

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Al Zaatari, A, El Ballouli, R, Elbassuoni, S, El-Hajj, W, Hajj, H, Shaban, K, Habash, N & Yehya, E 2016, Arabic corpora for credibility analysis. in Proceedings of the 10th International Conference on Language Resources and Evaluation, LREC 2016. European Language Resources Association (ELRA), pp. 4396-4401, 10th International Conference on Language Resources and Evaluation, LREC 2016, Portoroz, Slovenia, 5/23/16.
    Al Zaatari A, El Ballouli R, Elbassuoni S, El-Hajj W, Hajj H, Shaban K et al. Arabic corpora for credibility analysis. In Proceedings of the 10th International Conference on Language Resources and Evaluation, LREC 2016. European Language Resources Association (ELRA). 2016. p. 4396-4401
    Al Zaatari, Ayman ; El Ballouli, Rim ; Elbassuoni, Shady ; El-Hajj, Wassim ; Hajj, Hazem ; Shaban, Khaled ; Habash, Nizar ; Yehya, Emad. / Arabic corpora for credibility analysis. Proceedings of the 10th International Conference on Language Resources and Evaluation, LREC 2016. European Language Resources Association (ELRA), 2016. pp. 4396-4401
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    abstract = "A significant portion of data generated on blogging and microblogging websites is non-credible as shown in many recent studies. To filter out such non-credible information, machine learning can be deployed to build automatic credibility classifiers. However, as in the case with most supervised machine learning approaches, a sufficiently large and accurate training data must be available. In this paper, we focus on building a public Arabic corpus of blogs and microblogs that can be used for credibility classification. We focus on Arabic due to the recent popularity of blogs and microblogs in the Arab World and due to the lack of any such public corpora in Arabic. We discuss our data acquisition approach and annotation process, provide rigid analysis on the annotated data and finally report some results on the effectiveness of our data for credibility classification.",
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    AU - Elbassuoni, Shady

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    AU - Hajj, Hazem

    AU - Shaban, Khaled

    AU - Habash, Nizar

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