# Deep Learning Approach for Classifying the Aggressive Comments on Social Media: Machine Translated Data Vs Real Life Data

arXiv: https://arxiv.org/abs/2303.07484 · HTML: https://mlsanity.com/paper/2303.07484/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 43 references that are in the map.
- First posted on arXiv 13 Mar 2023.

## Builds on (most cited first, 6 of 6)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Publicly Available Clinical BERT Embeddings](https://mlsanity.com/paper/1904.03323/) (2019)
- [Fine-grained Sentiment Classification using BERT](https://mlsanity.com/paper/1910.03474/) (2019)
- [Developing a Multilingual Annotated Corpus of Misogyny and Aggression](https://mlsanity.com/paper/2003.07428/) (2020)
- [Leveraging Multilingual Transformers for Hate Speech Detection](https://mlsanity.com/paper/2101.03207/) (2021)
- [Cyberbullying Detection Using Deep Neural Network from Social Media Comments in Bangla Language](https://mlsanity.com/paper/2106.04506/) (2021)

## Built on by (most cited first, 4 of 4)

- [Exploring the Vulnerabilities of Machine Learning and Quantum Machine Learning to Adversarial Attacks using a Malware Dataset: A Comparative Analysis](https://mlsanity.com/paper/2305.19593/) (2023)
- [Case Study-Based Approach of Quantum Machine Learning in Cybersecurity: Quantum Support Vector Machine for Malware Classification and Protection](https://mlsanity.com/paper/2306.00284/) (2023)
- [Feature Engineering-Based Detection of Buffer Overflow Vulnerability in Source Code Using Neural Networks](https://mlsanity.com/paper/2306.07981/) (2023)
- [A Trustable LSTM-Autoencoder Network for Cyberbullying Detection on Social Media Using Synthetic Data](https://mlsanity.com/paper/2308.09722/) (2023)

## Abstract (arXiv)

Aggressive comments on social media negatively impact human life. Such offensive contents are responsible for depression and suicidal-related activities. Since online social networking is increasing day by day, the hate content is also increasing. Several investigations have been done on the domain of cyberbullying, cyberaggression, hate speech, etc. The majority of the inquiry has been done in the English language. Some languages (Hindi and Bangla) still lack proper investigations due to the lack of a dataset. This paper particularly worked on the Hindi, Bangla, and English datasets to detect aggressive comments and have shown a novel way of generating machine-translated data to resolve data unavailability issues. A fully machine-translated English dataset has been analyzed with the models such as the Long Short term memory model (LSTM), Bidirectional Long-short term memory model (BiLSTM), LSTM-Autoencoder, word2vec, Bidirectional Encoder Representations from Transformers (BERT), and generative pre-trained transformer (GPT-2) to make an observation on how the models perform on a machine-translated noisy dataset. We have compared the performance of using the noisy data with two more datasets such as raw data, which does not contain any noises, and semi-noisy data, which contains a certain amount of noisy data. We have classified both the raw and semi-noisy data using the aforementioned models. To evaluate the performance of the models, we have used evaluation metrics such as F1-score,accuracy, precision, and recall. We have achieved the highest accuracy on raw data using the gpt2 model, semi-noisy data using the BERT model, and fully machine-translated data using the BERT model. Since many languages do not have proper data availability, our approach will help researchers create machine-translated datasets for several analysis purposes.