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A Deep Learning Model for Textual Content Moderation Across Social Platforms Using LSTM and BERT
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Author(s): Mohd Adnan (Interdisciplinary Centre for Artificial Intelligence, Aligarh Muslim University, India), K. Bismil (Interdisciplinary Centre for Artificial Intelligence, Aligarh Muslim University, India), Mohd Imran (Khwaja Moinuddin Chishti Language University, Lucknow, India)and Tasleem Jamal (Khwaja Moinuddin Chishti Language University, Lucknow, India)
Copyright: 2027
Pages: 28
Source title:
Encyclopedia of Modern Artificial Intelligence
Source Author(s)/Editor(s): Mehdi Khosrow-Pour, D.B.A. (Founding Editor-in-Chief, Information Resources Management Journal (IRMJ), USA)
DOI: 10.4018/407629
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Abstract
The proliferation of user-generated content on digital platforms has introduced significant challenges related to content moderation, particularly the detection of toxic, hateful, and abusive language. Consequently, artificial intelligence and NLP techniques have emerged as promising solutions for automating this task. This article investigates the use of deep learning models specifically Long Short-Term Memory (LSTM) networks and Bidirectional Encoder Representations from Transformers (BERT) for automated toxic content classification. This study utilizes both models individually and in a hybrid configuration to classify comments into three categories: safe, abusive, and hateful. The experimental setup involves pre-processing the Jigsaw Toxic Comment Classification dataset, applying tokenization, stop word removal, and balancing techniques to improve class distribution. Evaluation metrics accuracy, precision, recall, and F1-score are used to assess model performance. Results indicate that BERT significantly outperforms LSTM, achieving an F1-score of 89.6% compared to LSTM's 88.18%.
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