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Do You Really Follow Them? Automatic Detection of Credulous Twitter Users

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Intelligent Data Engineering and Automated Learning – IDEAL 2019 (IDEAL 2019)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11871))

Abstract

Online Social Media represent a pervasive source of information able to reach a huge audience. Sadly, recent studies show how online social bots (automated, often malicious accounts, populating social networks and mimicking genuine users) are able to amplify the dissemination of (fake) information by orders of magnitude. Using Twitter as a benchmark, in this work we focus on what we define credulous users, i.e., human-operated accounts with a high percentage of bots among their followings. Being more exposed to the harmful activities of social bots, credulous users may run the risk of being more influenced than other users; even worse, although unknowingly, they could become spreaders of misleading information (e.g., by retweeting bots). We design and develop a supervised classifier to automatically recognize credulous users. The best tested configuration achieves an accuracy of 93.27% and AUC-ROC of 0.93, thus leading to positive and encouraging results.

Partially supported by the European Union’s Horizon 2020 programme (grant agreement No. 830892, SPARTA) and by IMT Scuola Alti Studi Lucca: Integrated Activity Project TOFFEe ‘TOols for Fighting FakEs’. It has also benefited from the computing resources (ULITE) provided by the IT division of LNGS in L’Aquila.

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Notes

  1. 1.

    Bot Repository Datasets: https://goo.gl/87Kzcr.

  2. 2.

    Twitter API: https://goo.gl/njcjr1.

  3. 3.

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

  4. 4.

    Complete Automation Probability: https://tinyurl.com/yxp3wqzh.

  5. 5.

    English/Universal Score: https://tinyurl.com/y2skbmqc.

  6. 6.

    ClassA features require only information available in the profile of the account [10].

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Author information

Authors and Affiliations

  1. Gran Sasso Science Institute, L’Aquila, Italy

    Alessandro Balestrucci & Catia Trubiani

  2. IMT School for Advanced Studies, Lucca, Italy

    Rocco De Nicola & Marinella Petrocchi

  3. Institute of Informatics and Telematics (IIT-CNR), Pisa, Italy

    Marinella Petrocchi

Authors
  1. Alessandro Balestrucci
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  2. Rocco De Nicola
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  3. Marinella Petrocchi
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  4. Catia Trubiani
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Corresponding author

Correspondence to Alessandro Balestrucci .

Editor information

Editors and Affiliations

  1. University of Manchester, Manchester, UK

    Hujun Yin

  2. Technical University of Madrid, Madrid, Spain

    David Camacho

  3. University of Birmingham, Birmingham, UK

    Peter Tino

  4. University of Huelva, Huelva, Spain

    Antonio J. Tallón-Ballesteros

  5. University of Exeter, Exeter, UK

    Ronaldo Menezes

  6. University of Manchester, Manchester, UK

    Richard Allmendinger

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Balestrucci, A., De Nicola, R., Petrocchi, M., Trubiani, C. (2019). Do You Really Follow Them? Automatic Detection of Credulous Twitter Users. In: Yin, H., Camacho, D., Tino, P., Tallón-Ballesteros, A., Menezes, R., Allmendinger, R. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2019. IDEAL 2019. Lecture Notes in Computer Science(), vol 11871. Springer, Cham. https://doi.org/10.1007/978-3-030-33607-3_44

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  • DOI: https://doi.org/10.1007/978-3-030-33607-3_44

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-33606-6

  • Online ISBN: 978-3-030-33607-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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