Sallau, Mullah Nanlir
(2023)
Enhanced Heterogeneous Stacked Ensemble Machine Learning Model For Detecting Nigerian Politically Motivated Cyberhate.
PhD thesis, Perpustakaan Hamzah Sendut.
Abstract
Hate speech is a universal problem from time immemorial. The high adoption of social media (SM) has made it a problem of gigantic proportions during elections in Nigeria. The anonymity enjoyed by the users is the main reason for the high volume of cyber hate in Nigeria's social media space. Politicians usually circulate different politically motivated hate messages on social media during elections. Though, different artificial intelligence (AI) approaches such as machine learning models have been developed to address the problem with reasonable success. Nonetheless, the problem persists and leads to a high rate of cyberhate crime in Nigeria. The main problem is the lack of research to build models to address peculiarities in Nigeria. These problems made existing models incapacitated in Nigeria's cyberspace. To solve the identified research gaps from the vantage point of a machine learning researcher, the problem was modelled as a text classification task. To achieve the main objective, the study proposed to enhance a technique called the stacking ensemble method. The proposed method is called the heterogeneous stacked ensemble (HSE).
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