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Bots and Gender Detection on Twitter Using Stylistic Features

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Advances in Computational Collective Intelligence (ICCCI 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1653))

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Abstract

This paper describes our proposed method for the author profiling task at PAN 2019. The aim of this task is to identify the type of a Twitter user (i.e. bot or human). Then, in case of a human, determine its gender (i.e. male or female). Our approach uses a set of language-independent features and it applies machine learning algorithms. After an in-depth experimental study, conducted on English and Spanish datasets, we show that by using a simple set of stylistic information, we can surpass other existing methods that mainly depend on the content of the tweets. For the English dataset, accuracies of 93.06% and 90.04% are obtained for bot an gender classification tasks respectively. Using Spanish tweets, accuracies of 90.53% and 89.11% are achieved for bot and gender detection task respectively.

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Author information

Authors and Affiliations

  1. MARS Research Laboratory, LR 17ES05, University of Sousse, Sousse, Tunisia

    Sarra Ouni, Fethi Fkih & Mohamed Nazih Omri

  2. Department of Computer Science, College of Computer, Qassim University, Buraydah, Saudi Arabia

    Fethi Fkih

Authors
  1. Sarra Ouni
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  2. Fethi Fkih
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  3. Mohamed Nazih Omri
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Corresponding author

Correspondence to Sarra Ouni .

Editor information

Editors and Affiliations

  1. University of Craiova, Craiova, Romania

    Costin Bădică

  2. Vrije Universiteit Amsterdam, Amsterdam, The Netherlands

    Jan Treur

  3. Claude Bernard University Lyon 1, Villeurbanne Cedex, France

    Djamal Benslimane

  4. Wrocław University of Science and Technology, Wrocław, Poland

    Bogumiła Hnatkowska

  5. Wrocław University of Science and Technology, Wrocław, Poland

    Marek Krótkiewicz

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Ouni, S., Fkih, F., Omri, M.N. (2022). Bots and Gender Detection on Twitter Using Stylistic Features. In: Bădică, C., Treur, J., Benslimane, D., Hnatkowska, B., Krótkiewicz, M. (eds) Advances in Computational Collective Intelligence. ICCCI 2022. Communications in Computer and Information Science, vol 1653. Springer, Cham. https://doi.org/10.1007/978-3-031-16210-7_53

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  • DOI: https://doi.org/10.1007/978-3-031-16210-7_53

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-16209-1

  • Online ISBN: 978-3-031-16210-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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