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This is a Quantitative Study study.

October 27, 2025

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

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Authors

JMJiahuai MaUniversity of Florida Health KXKaixian XuHudson Institute YQYu QiaoChongqing University of Posts and Telecommunications

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Implication

Novel approach combines neural network feature extraction with machine learning algorithms, enhancing toxic comments classification while addressing overfitting challenges.

Key Points

The model integrates neural network capabilities with traditional machine learning algorithms for better detection.
Achieved an accuracy of 89.78% on the Kaggle dataset, outperforming individual models in effectiveness.
High-dimensional feature extraction is key to improving classification of social media toxic comments.
Efforts are needed to tackle challenges such as overfitting and interpretability to enhance performance.

Cite This Study

Ma et al. (2022) studied this question.

synapsesocial.com/papers/68ff87f5c8c50a61f2bdd892 https://doi.org/10.62836/jcmea.v2i1.0005

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Implication

Novel approach combines neural network feature extraction with machine learning algorithms, enhancing toxic comments classification while addressing overfitting challenges.

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

Discussion

Member takes

Implication

Key Points

Cite This Study

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

Discussion

Member takes

Implication

Key Points

Cite This Study

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