Arabic corpora for credibility analysis

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

Arabic corpora for credibility analysis

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

Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference 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 language	English (US)
Title of host publication	Proceedings of the 10th International Conference on Language Resources and Evaluation, LREC 2016
Editors	Nicoletta Calzolari, Khalid Choukri, Helene Mazo, Asuncion Moreno, Thierry Declerck, Sara Goggi, Marko Grobelnik, Jan Odijk, Stelios Piperidis, Bente Maegaard, Joseph Mariani
Publisher	European Language Resources Association (ELRA)
Pages	4396-4401
Number of pages	6
ISBN (Electronic)	9782951740891
State	Published - 2016
Event	10th International Conference on Language Resources and Evaluation, LREC 2016 - Portoroz, Slovenia Duration: May 23 2016 → May 28 2016

Publication series

Name	Proceedings of the 10th International Conference on Language Resources and Evaluation, LREC 2016

Other

Other	10th International Conference on Language Resources and Evaluation, LREC 2016
Country/Territory	Slovenia
City	Portoroz
Period	5/23/16 → 5/28/16

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., Habash, N., & Yehya, E. (2016). Arabic corpora for credibility analysis. In N. Calzolari, K. Choukri, H. Mazo, A. Moreno, T. Declerck, S. Goggi, M. Grobelnik, J. Odijk, S. Piperidis, B. Maegaard, & J. Mariani (Eds.), Proceedings of the 10th International Conference on Language Resources and Evaluation, LREC 2016 (pp. 4396-4401). (Proceedings of the 10th International Conference on Language Resources and Evaluation, LREC 2016). European Language Resources Association (ELRA).

Arabic corpora for credibility analysis. / Al Zaatari, Ayman; El Ballouli, Rim; Elbassuoni, Shady et al.
Proceedings of the 10th International Conference on Language Resources and Evaluation, LREC 2016. ed. / Nicoletta Calzolari; Khalid Choukri; Helene Mazo; Asuncion Moreno; Thierry Declerck; Sara Goggi; Marko Grobelnik; Jan Odijk; Stelios Piperidis; Bente Maegaard; Joseph Mariani. European Language Resources Association (ELRA), 2016. p. 4396-4401 (Proceedings of the 10th International Conference on Language Resources and Evaluation, LREC 2016).

Research output: Chapter in Book/Report/Conference proceeding › Conference 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 N Calzolari, K Choukri, H Mazo, A Moreno, T Declerck, S Goggi, M Grobelnik, J Odijk, S Piperidis, B Maegaard & J Mariani (eds), Proceedings of the 10th International Conference on Language Resources and Evaluation, LREC 2016. 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 Calzolari N, Choukri K, Mazo H, Moreno A, Declerck T, Goggi S, Grobelnik M, Odijk J, Piperidis S, Maegaard B, Mariani J, editors, Proceedings of the 10th International Conference on Language Resources and Evaluation, LREC 2016. European Language Resources Association (ELRA). 2016. p. 4396-4401. (Proceedings of the 10th International Conference on Language Resources and Evaluation, LREC 2016).

Al Zaatari, Ayman ; El Ballouli, Rim ; Elbassuoni, Shady et al. / Arabic corpora for credibility analysis. Proceedings of the 10th International Conference on Language Resources and Evaluation, LREC 2016. editor / Nicoletta Calzolari ; Khalid Choukri ; Helene Mazo ; Asuncion Moreno ; Thierry Declerck ; Sara Goggi ; Marko Grobelnik ; Jan Odijk ; Stelios Piperidis ; Bente Maegaard ; Joseph Mariani. European Language Resources Association (ELRA), 2016. pp. 4396-4401 (Proceedings of the 10th International Conference on Language Resources and Evaluation, LREC 2016).

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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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note = "Funding Information: This work was made possible by grant NPRP 6-716-1-138 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors.; 10th International Conference on Language Resources and Evaluation, LREC 2016 ; Conference date: 23-05-2016 Through 28-05-2016",

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AU - Al Zaatari, Ayman

AU - El Ballouli, Rim

AU - Elbassuoni, Shady

AU - El-Hajj, Wassim

AU - Hajj, Hazem

AU - Shaban, Khaled

AU - Habash, Nizar

AU - Yehya, Emad

N1 - Funding Information: This work was made possible by grant NPRP 6-716-1-138 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors.

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N2 - 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.

AB - 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.

KW - Blogs

KW - Credibility

KW - Crowdsourcing

KW - Twitter

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BT - Proceedings of the 10th International Conference on Language Resources and Evaluation, LREC 2016

A2 - Calzolari, Nicoletta

A2 - Choukri, Khalid

A2 - Mazo, Helene

A2 - Moreno, Asuncion

A2 - Declerck, Thierry

A2 - Goggi, Sara

A2 - Grobelnik, Marko

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PB - European Language Resources Association (ELRA)

T2 - 10th International Conference on Language Resources and Evaluation, LREC 2016

Y2 - 23 May 2016 through 28 May 2016

ER -

Arabic corpora for credibility analysis

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Keywords

ASJC Scopus subject areas

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