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VOL. 7, ISSUE 1 (2023)
Development and evaluation of a framework for detecting hate speech and abusive language in Zambia using machine learning
Authors
Clement Mulenga Sinyangwe, Douglas Kunda, William Abwino Phiri
Abstract
The advent of artificial intelligence (AI) has revolutionized various
fields, including information technology, intelligent transportation systems,
virtual personal assistants, robotic surgery, and natural language processing
(NLP) applications. However, along with the numerous benefits brought about by
technological advancements, there are also drawbacks, such as the widespread
dissemination of abusive language, fake news, and hate speech, which can easily
be propagated in the digital world. Social media platforms like Facebook and
Twitter have played a significant role in the rapid spread of rumors,
conspiracy theories, hatred, xenophobia, racism, and prejudice. The misuse of
technology has not only influenced public opinion but also impacted religious
views worldwide, enabling targeting of individuals based on various attributes.
Zambia's social media landscape has witnessed a dynamic shift, particularly
following the transition of the government in 2021, which has led to greater
freedom of expression but also an upsurge in hate speech and abusive language
associated with political, ethnic, and religious divisions. The freedom of
expression (FoE) in Zambia has facilitated the sharing of diverse views and
ideas, contributing to development, democracy, and dialogue. However, this
freedom has also led to the proliferation of hate speech on various online
platforms, including social media. Despite the efforts of governments, the
technology industry, and individual researchers to address the issue of hate
speech, challenges persist. Legislative measures have been attempted to
suppress hate speech, but their effectiveness is often limited. The main
objective of this study was to develop and evaluate the framework for detecting
hate speech and abusive language in Zambia. Cross-Industry
Standard Procedure for Data Mining (CRISP-DM) methodology, a commonly used
method for overseeing data science projects, was used to perform this study. precision, recall, and F1 score was used to evaluate the framework. Gradient-boosted
decision tree was picked over the other algorithms (KNeighbors Classifier,
logistic Regression, Random Forest, Decision Tree and Naïve Bayes) because
apart from being a powerful machine learning algorithm that has become
increasingly popular in recent years, especially in tasks such as
classification and regression.
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Pages:23-29
How to cite this article:
Clement Mulenga Sinyangwe, Douglas Kunda, William Abwino Phiri "Development and evaluation of a framework for detecting hate speech and abusive language in Zambia using machine learning". International Journal of Advanced Engineering and Technology, Vol 7, Issue 1, 2023, Pages 23-29
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