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Bias Detection and Mitigation in Textual Data: A Study on Fake News and Hate Speech Detection

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Advances in Information Retrieval (ECIR 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14610))

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Abstract

Addressing bias in NLP-based solutions is crucial to promoting fairness, avoiding discrimination, building trust, upholding ethical standards, and ultimately improving their performance and reliability. On the topic of bias detection and mitigation in textual data, this work examines the effect of different bias detection models along with standard debiasing methods on the effectiveness of fake news and hate speech detection tasks. Extensive discussion of the results draws useful conclusions, highlighting the inherent difficulties in effectively managing bias.

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Acknowledgements

This project has received funding from the European Union’s H2020 research and innovation programme as part of the STARLIGHT (GA No 101021797) project.

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Authors and Affiliations

  1. Information Technologies Institute, Centre for Research and Technology Hellas, Thessaloniki, Greece

    Apostolos Kasampalis, Despoina Chatzakou, Theodora Tsikrika, Stefanos Vrochidis & Ioannis Kompatsiaris

Authors
  1. Apostolos Kasampalis
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  2. Despoina Chatzakou
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  3. Theodora Tsikrika
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  4. Stefanos Vrochidis
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  5. Ioannis Kompatsiaris
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Corresponding author

Correspondence to Despoina Chatzakou .

Editor information

Editors and Affiliations

  1. Georgetown University, Washington, WA, USA

    Nazli Goharian

  2. University of Pisa, PISA, Pisa, Italy

    Nicola Tonellotto

  3. King's College London, London, UK

    Yulan He

  4. University College London, London, UK

    Aldo Lipani

  5. University of Glasgow, Glasgow, UK

    Graham McDonald

  6. University of Glasgow, Glasgow, UK

    Craig Macdonald

  7. University of Glasgow, Glasgow, UK

    Iadh Ounis

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Kasampalis, A., Chatzakou, D., Tsikrika, T., Vrochidis, S., Kompatsiaris, I. (2024). Bias Detection and Mitigation in Textual Data: A Study on Fake News and Hate Speech Detection. In: Goharian, N., et al. Advances in Information Retrieval. ECIR 2024. Lecture Notes in Computer Science, vol 14610. Springer, Cham. https://doi.org/10.1007/978-3-031-56063-7_29

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  • DOI: https://doi.org/10.1007/978-3-031-56063-7_29

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-56062-0

  • Online ISBN: 978-3-031-56063-7

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