Gradient Boosted Regression Tree Approach to Predicting Toxic Interactions on X and YouTube

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Notice

nThis is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.n

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Year : 2025 [if 2224 equals=””]24/09/2025 at 1:46 PM[/if 2224] | [if 1553 equals=””] Volume : 15 [else] Volume : 15[/if 1553] | [if 424 equals=”Regular Issue”]Issue : [/if 424][if 424 equals=”Special Issue”]Special Issue[/if 424] [if 424 equals=”Conference”][/if 424] 03 | Page : 7 14

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    Senthil P., Aniruthya A., Harini S., Rahaswedha K.,

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  1. Assistant Professor, Student, Student, Student, Department of Computer Science and Engineering, Karpagam College of Engineering, Coimbatore, Department of Computer Science and Engineering, Karpagam College of Engineering, Coimbatore, Department of Computer Science and Engineering, Karpagam College of Engineering, Coimbatore, Department of Computer Science and Engineering, Karpagam College of Engineering, Coimbatore, Tamil Nadu, Tamil Nadu, Tamil Nadu, Tamil Nadu, India, India, India, India

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Abstract

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nIn the digital age, social media platforms play a vital role in facilitating user engagement, encompassing both positive interactions and avenues for negative, often harmful behaviors. Recognizing and addressing toxic exchanges is paramount to nurturing healthy online communities and preserving users’ well-being. This study introduces a novel method for identifying toxic interactions by utilizing Gradient Boosting Regression Trees (GBRT) algorithm, a machine learning approach renowned for its exceptional accuracy and ability to handle intricate, non-linear data relationships. The proposed GBRT compares five traditional classification techniques, such as Logistic Regression (LR), Random Forests (RF), Support Vector Machine (SVM), Naïve Bayes (NB) and SGD Classifier (Stochastic Gradient Descent) which are commonly employed in toxicity identification endeavors. The comparative analysis is done using metrics like accuracy, precision, recall, and F1-score and the results show that the GBRT outperforms other compared algorithms with its overall performance. Respectively, the precision rates of GBRT, SVM, RF, LR, NB and SGD Classifier are 96, 94, 89, 88, 85, and 81%; accuracy rates of GBRT, RF, SVM, LR, NB and SGD Classifier are 95, 93, 89, 83, 80, and 78%; recall rates of GBRT, RF, SVM, NB, LR and SGD Classifier are 95, 92, 90, 87, 84, and 81%; F1-scores of GBRT, RF, SVM, LR, NB and SGD Classifier are 94, 91, 89, 86, 83, and 80%. The outcomes are achieved by conducting extensive trials on publicly available social media datasets, such as the Final Balanced Dataset and Youtoxic with the size of 57746.nn

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Keywords: Toxic comment detection, X, YouTube, gradient boosting regression trees, logistic regression, random forests, support vector machine, Naive Bayes and SGD classifier, machine learning

n[if 424 equals=”Regular Issue”][This article belongs to Trends in Opto-electro & Optical Communication ]

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[/if 424][if 424 equals=”Special Issue”][This article belongs to Special Issue under section in Trends in Opto-electro & Optical Communication (toeoc)][/if 424][if 424 equals=”Conference”]This article belongs to Conference [/if 424]

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How to cite this article:
nSenthil P., Aniruthya A., Harini S., Rahaswedha K.. [if 2584 equals=”][226 wpautop=0 striphtml=1][else]Gradient Boosted Regression Tree Approach to Predicting Toxic Interactions on X and YouTube[/if 2584]. Trends in Opto-electro & Optical Communication. 10/09/2025; 15(03):7-14.

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nSenthil P., Aniruthya A., Harini S., Rahaswedha K.. [if 2584 equals=”][226 striphtml=1][else]Gradient Boosted Regression Tree Approach to Predicting Toxic Interactions on X and YouTube[/if 2584]. Trends in Opto-electro & Optical Communication. 10/09/2025; 15(03):7-14. Available from: https://journals.stmjournals.com/toeoc/article=10/09/2025/view=0

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[if 424 not_equal=””]Regular Issue[else]Published[/if 424] Subscription Review Article

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Volume 15
[if 424 equals=”Regular Issue”]Issue[/if 424][if 424 equals=”Special Issue”]Special Issue[/if 424] [if 424 equals=”Conference”][/if 424] 03
Received 14/06/2025
Accepted 19/06/2025
Published 10/09/2025
Retracted
Publication Time 88 Days

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