Unintended bias evaluation: an analysis of hate speech detection and gender bias mitigation on social media using ensemble learning.

NASCIMENTO, Francimara, CAVALCANTI, George and DA COSTA ABREU, Marjory (2022). Unintended bias evaluation: an analysis of hate speech detection and gender bias mitigation on social media using ensemble learning. Expert Systems with Applications, 201: 117032.

[img] PDF
Da Costa-Abreu-UnintendedBiasEvaluation(AM).pdf - Accepted Version
Restricted to Repository staff only until 14 April 2023.
Creative Commons Attribution Non-commercial No Derivatives.

Download (429kB)
Official URL: https://www.sciencedirect.com/science/article/pii/...
Link to published version:: https://doi.org/10.1016/j.eswa.2022.117032
Related URLs:

    Abstract

    Hate speech on online social media platforms is now at a level that has been considered a serious concern by governments, media outlets, and scientists, especially because it is easily spread, promoting harm to individuals and society, and made it virtually impossible to tackle with using just human analysis. Automatic approaches using machine learning and natural language processing are helpful for detection. For such applications, amongst several different approaches, it is essential to investigate the systems’ robustness to deal with biases towards identity terms (gender, race, religion, for example). In this work, we analyse gender bias in different datasets and proposed a ensemble learning approach based on different feature spaces for hate speech detection with the aim that the model can learn from different abstractions of the problem, namely unintended bias evaluation metrics. We have used nine different feature spaces to train the pool of classifiers and evaluated our approach on a publicly available corpus, and our results demonstrate its effectiveness compared to state-of-the-art solutions.

    Item Type: Article
    Uncontrolled Keywords: 01 Mathematical Sciences; 08 Information and Computing Sciences; 09 Engineering; Artificial Intelligence & Image Processing
    Identification Number: https://doi.org/10.1016/j.eswa.2022.117032
    SWORD Depositor: Symplectic Elements
    Depositing User: Symplectic Elements
    Date Deposited: 28 Mar 2022 11:15
    Last Modified: 09 May 2022 10:04
    URI: http://shura.shu.ac.uk/id/eprint/30005

    Actions (login required)

    View Item View Item

    Downloads

    Downloads per month over past year

    View more statistics