Comparing Machine Learning and Deep Learning Techniques for Text Analytics: Detecting the Severity of Hate Comments Online

Alaa Marshan (), Farah Nasreen Mohamed Nizar, Athina Ioannou and Konstantina Spanaki
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Alaa Marshan: University of Surrey
Farah Nasreen Mohamed Nizar: Brunel University
Athina Ioannou: University of Surrey
Konstantina Spanaki: Audencia Business School

Information Systems Frontiers, 2025, vol. 27, issue 2, No 6, 487-505

Abstract: Abstract Social media platforms have become an increasingly popular tool for individuals to share their thoughts and opinions with other people. However, very often people tend to misuse social media posting abusive comments. Abusive and harassing behaviours can have adverse effects on people's lives. This study takes a novel approach to combat harassment in online platforms by detecting the severity of abusive comments, that has not been investigated before. The study compares the performance of machine learning models such as Naïve Bayes, Random Forest, and Support Vector Machine, with deep learning models such as Convolutional Neural Network (CNN) and Bi-directional Long Short-Term Memory (Bi-LSTM). Moreover, in this work we investigate the effect of text pre-processing on the performance of the machine and deep learning models, the feature set for the abusive comments was made using unigrams and bigrams for the machine learning models and word embeddings for the deep learning models. The comparison of the models’ performances showed that the Random Forest with bigrams achieved the best overall performance with an accuracy of (0.94), a precision of (0.91), a recall of (0.94), and an F1 score of (0.92). The study develops an efficient model to detect severity of abusive language in online platforms, offering important implications both to theory and practice.

Keywords: Machine learning; Deep learning; Hate speech; Social media; Text pre-processing; Text representation; Text analytics (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s10796-023-10446-x

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