Hate Speech Detection Using Genetic Programming
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gp-bibliography.bib Revision:1.7964
- @InProceedings{Aljero:2020:ICOASE,
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author = "Mona Khalifa A. Aljero and Nazife Dimililer",
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title = "Hate Speech Detection Using Genetic Programming",
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booktitle = "2020 International Conference on Advanced Science and
Engineering (ICOASE)",
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year = "2020",
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abstract = "There has been a steep increase in the use of social
media in our everyday lives in recent years. Along with
this, there has been an increase in hate speech
disseminated on these platforms, due to the anonymity
of the users as well as the ease of use. Social media
platforms need to filter and prevent the spread of hate
speech to protect their users and society. Due to the
high traffic, automatic detection of hate speech is
necessary. Hate speech detection is one of the most
difficult classification challenges in text mining.
Research in this domain focuses on the use of
supervised machine learning approaches, such as support
vector machine, logistic regression, convolutional
neural network, and random forest. Ensemble techniques
have also been employed. However, the performance of
these approaches has not yet reached an acceptable
level. In this paper, we propose the use of the Genetic
Programming (GP) approach for binary classification of
hate speech on social media platforms. Each individual
in the GP framework represents a classifier that is
evolved to optimize Fl-score. Experimental results show
the effectiveness of our GP approach; the proposed
approach outperforms the state-of-the-art using the
same dataset HatEval.",
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keywords = "genetic algorithms, genetic programming",
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DOI = "doi:10.1109/ICOASE51841.2020.9436621",
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month = dec,
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notes = "Also known as \cite{9436621}",
- }
Genetic Programming entries for
Mona Khalifa A Aljero
Nazife Dimililer
Citations