# Semantic and Contextual Modeling for Malicious Comment Detection with BERT-BiLSTM

arXiv: https://arxiv.org/abs/2503.11084 · HTML: https://mlsanity.com/paper/2503.11084/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Placed in Safety alignment, refusal and guardrails, one of 12 directions in LLM safety and security.
- Builds on 10 of its 26 references that are in the map.
- First posted on arXiv 14 Mar 2025.

## Placed in

- LLM safety and security / [Safety alignment, refusal and guardrails](https://mlsanity.com/safety/safety-alignment-refusal-and-guardrails/)

## Builds on (most cited first, 10 of 10)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Transforming Multidimensional Time Series into Interpretable Event Sequences for Advanced Data Mining](https://mlsanity.com/paper/2409.14327/) (2024)
- [Transformer based Automatic COVID-19 Fake News Detection System](https://mlsanity.com/paper/2101.00180/) (2021)
- [Contrastive Learning for Cold Start Recommendation with Adaptive Feature Fusion](https://mlsanity.com/paper/2502.03664/) (2025)
- [Graph Neural Network-Driven Hierarchical Mining for Complex Imbalanced Data](https://mlsanity.com/paper/2502.03803/) (2025)
- [Optimized Unet with Attention Mechanism for Multi-Scale Semantic Segmentation](https://mlsanity.com/paper/2502.03813/) (2025)
- [A Structured Reasoning Framework for Unbalanced Data Classification Using Probabilistic Models](https://mlsanity.com/paper/2502.03386/) (2025)
- [Multi-Scale Transformer Architecture for Accurate Medical Image Classification](https://mlsanity.com/paper/2502.06243/) (2025)
- [A Deep Learning Approach to Interface Color Quality Assessment in HCI](https://mlsanity.com/paper/2502.09914/) (2025)
- [A Hybrid CNN-Transformer Model for Heart Disease Prediction Using Life History Data](https://mlsanity.com/paper/2503.02124/) (2025)

## Built on by (most cited first, 5 of 5)

- [Structured Memory Mechanisms for Stable Context Representation in Large Language Models](https://mlsanity.com/paper/2505.22921/) (2025)
- [Context-Aware Adaptive Sampling for Intelligent Data Acquisition Systems Using DQN](https://mlsanity.com/paper/2504.09344/) (2025)
- [Capsule Network-Based Semantic Intent Modeling for Human-Computer Interaction](https://mlsanity.com/paper/2507.00540/) (2025)
- [A Deep Learning Framework for Boundary-Aware Semantic Segmentation](https://mlsanity.com/paper/2503.22050/) (2025)
- [Improving Harmful Text Detection with Joint Retrieval and External Knowledge](https://mlsanity.com/paper/2504.02310/) (2025)

## Abstract (arXiv)

This study aims to develop an efficient and accurate model for detecting malicious comments, addressing the increasingly severe issue of false and harmful content on social media platforms. We propose a deep learning model that combines BERT and BiLSTM. The BERT model, through pre-training, captures deep semantic features of text, while the BiLSTM network excels at processing sequential data and can further model the contextual dependencies of text. Experimental results on the Jigsaw Unintended Bias in Toxicity Classification dataset demonstrate that the BERT+BiLSTM model achieves superior performance in malicious comment detection tasks, with a precision of 0.94, recall of 0.93, and accuracy of 0.94. This surpasses other models, including standalone BERT, TextCNN, TextRNN, and traditional machine learning algorithms using TF-IDF features. These results confirm the superiority of the BERT+BiLSTM model in handling imbalanced data and capturing deep semantic features of malicious comments, providing an effective technical means for social media content moderation and online environment purification.