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Bot Detection in Twitter: An Overview

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Flexible Query Answering Systems (FQAS 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14113))

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Abstract

Bot detection in social media, particularly on Twitter, has become a crucial issue in recent years due to the increasing use of bots for malicious uses such as the spreading of false information in order to manipulate public opinion. In this paper, we review the most widely available tools for bot detection and the categorization models that exist in the literature. This paper put focus on providing a concise and informative overview of state-of-the-art bot detection on Twitter. This overview can be useful for developing more effective detection methods. Overall, our paper provides valuable insights into the current state of bot detection in social media, suggesting new challenges and possible future trends and research.

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Acknowledgment

The research reported in this paper was supported by the DesinfoScan project: Grant TED2021-129402B-C21 funded by MCIN/AEI/10.13039/501100011033 and, by the European Union NextGenerationEU/PRTR, and FederaMed project: Grant PID2021-123960OB-I00 funded by MCIN/AEI/10.13039/501100011033 and by ERDF A way of making Europe. Finally the project is also partially supported by the Spanish Ministry of Education, Culture and Sport (FPU18/00150).

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Authors and Affiliations

  1. Department of Computer Science and A.I., University of Granada, Granada, Spain

    Salvador Lopez-Joya, J. Angel Diaz-Garcia, M. Dolores Ruiz & Maria J. Martin-Bautista

Authors
  1. Salvador Lopez-Joya
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  2. J. Angel Diaz-Garcia
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  3. M. Dolores Ruiz
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  4. Maria J. Martin-Bautista
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Corresponding author

Correspondence to Salvador Lopez-Joya .

Editor information

Editors and Affiliations

  1. Legind Technologies, Esbjerg V, Denmark

    Henrik Legind Larsen

  2. University of Granada, Granada, Spain

    Maria J. Martin-Bautista

  3. University of Granada, Granada, Spain

    M. Dolores Ruiz

  4. Roskilde University, Roskilde, Denmark

    Troels Andreasen

  5. Consiglio Nazionale delle Ricerche, Milano, Italy

    Gloria Bordogna

  6. Ghent University, Gent, Belgium

    Guy De Tré

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Lopez-Joya, S., Diaz-Garcia, J.A., Ruiz, M.D., Martin-Bautista, M.J. (2023). Bot Detection in Twitter: An Overview. In: Larsen, H.L., Martin-Bautista, M.J., Ruiz, M.D., Andreasen, T., Bordogna, G., De Tré, G. (eds) Flexible Query Answering Systems. FQAS 2023. Lecture Notes in Computer Science(), vol 14113. Springer, Cham. https://doi.org/10.1007/978-3-031-42935-4_11

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  • DOI: https://doi.org/10.1007/978-3-031-42935-4_11

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