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Spam Detection on Arabic Twitter

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Social Informatics (SocInfo 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12467))

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Abstract

Twitter has become a popular social media platform in the Arab region. Some users exploit this popularity by posting unwanted advertisements for their own interest. In this paper, we present a large manually annotated dataset of advertisement (Spam) tweets in Arabic. We analyze the characteristics of these tweets that distinguish them from other tweets and identify their targets and topics. In addition, we analyze the characteristics of Spam accounts. We utilize Support Vector Machines (SVMs) and contextual embedding based models to identify these Spam tweets with macro averaged F1 score above 98%.

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Notes

  1. 1.

    ads.twitter.com.

  2. 2.

    Dataset can be downloaded from http://alt.qcri.org/resources/SpamArabic-Twitter.tgz.

  3. 3.

    https://developer.twitter.com/en/docs/basics/counting-characters.

  4. 4.

    https://www.bbc.co.uk/usingthebbc/terms/what-are-the-rules-for-commenting/.

  5. 5.

    https://en.wikipedia.org/wiki/List_of_ISO_3166_country_codes.

  6. 6.

    https://help.twitter.com/en/rules-and-policies/twitter-rules.

  7. 7.

    https://botometer.iuni.iu.edu/.

  8. 8.

    We used the libSVM implementation in scikit-learn https://scikit-learn.org/.

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Authors and Affiliations

  1. Qatar Computing Research Institute, Doha, Qatar

    Hamdy Mubarak, Ahmed Abdelali, Sabit Hassan & Kareem Darwish

Authors
  1. Hamdy Mubarak
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  2. Ahmed Abdelali
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  3. Sabit Hassan
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  4. Kareem Darwish
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Corresponding author

Correspondence to Sabit Hassan .

Editor information

Editors and Affiliations

  1. Max Planck Institute for Demographic Research, Rostock, Germany

    Samin Aref

  2. University of Sheffield, Sheffield, UK

    Kalina Bontcheva

  3. King’s College London, London, UK

    Marco Braghieri

  4. UmeĂĄ University, UmeĂĄ, Sweden

    Frank Dignum

  5. ISTI-CNR, Pisa, Italy

    Fosca Giannotti

  6. University of Pisa, Pisa, Italy

    Francesco Grisolia

  7. University of Pisa, Pisa, Italy

    Dino Pedreschi

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Mubarak, H., Abdelali, A., Hassan, S., Darwish, K. (2020). Spam Detection on Arabic Twitter. In: Aref, S., et al. Social Informatics. SocInfo 2020. Lecture Notes in Computer Science(), vol 12467. Springer, Cham. https://doi.org/10.1007/978-3-030-60975-7_18

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  • DOI: https://doi.org/10.1007/978-3-030-60975-7_18

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