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https://doi.org/10.62836/jcmea.v2i1.0005
Jiahuai Ma, Kaixian Xu, Yu Qiao, & Zhaoyan Zhang. (2022). An Integrated Model for Social Media Toxic Comments Detection: Fusion of High-Dimensional Neural Network Representations and Multiple Traditional Machine Learning Algorithms. Journal of Computational Methods in Engineering Applications, 2(1), 1–12. https://doi.org/10.62836/jcmea.v2i1.0005
Volume 2, Issue 1, 2022

An Integrated Model for Social Media Toxic Comments Detection: Fusion of High-Dimensional Neural Network Representations and Multiple Traditional Machine Learning Algorithms

Social media platforms have become pivotal for global communication and information exchange but are increasingly challenged by the proliferation of toxic comments. These comments, characterized by abusive, discriminatory, or harassing language, threaten user safety and well-being, necessitating efficient detection systems. This paper proposes a novel hybrid approach to detect social media toxic comments by combining the feature extraction capabilities of Long Short Term Memoery (LSTM)-based neural networks with multiple machine learning models, including Random Forest, Logistic Regression, and K-Nearest Neighbors. High-dimensional feature representations from the neural network are integrated with predictions from traditional classifiers, and Random Forest optimizes the output weights to maximize performance. Evaluated on a Kaggle dataset, the proposed model achieves an accuracy of 89.78% and outperforms individual models in handling the complexity of toxic comments. However, challenges such as overfitting, computational overhead, and interpretability remain. Future work aims to address these limitations through improved data augmentation, explainability methods, and more scalable architectures.

component; multi-model fusion; social media toxic comments detection; machine learning

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Supporting Agencies

  1. Funding: Not applicable.