Cyberbullying Detection using LSTM-CNN architecture and its applications
Abstract
In the present information age, there's been an increase in the use of social media, as a result, there have been multiple cyberbullying instances. To prevent or reduce cyberbullying, many existing approaches in the literature incorporate normal Machine Learning and Natural Language Processing text classification models without considering the sentence semantics. In this paper, we aim to target that issue. We have used word2vec to train our custom word embeddings upon which we built our LSTM - CNN architecture and finally it was trained on it. We made a comparative study of the above approaches by testing our model on Twitter posts and comments. The eminent performance achieved by our method has been observed in this study. We also created a web application that used the model to classify tweets as cyberbullying or not based on the toxicity score along with various features. The model was also implemented on Telegram Bot which is used to check and prevent cyberbullying. Two Chrome Extensions were built to redact and retract Not Safe For Work (NSFW) content and prevent cyberbullying on WhatsApp Web. We have been able to achieve excellent performance in the form of a 97% ROC AUC score for our model.
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LSTM-CNN performs better than CNN-LSTM in cyberbullying detection, based on experiments in [63].
“In [63], we found both CNN-LSTM and LSTM-CNN experiments, and they showed that LSTM-CNN performs better than CNN-LSTM because the CNN layer would receive the word embeddings as input, which will further be pooled to a smaller dimension, and then, the LSTM layer will use the ordering of said features to learn about the input's text ordering.”
From A Review on Deep-Learning-Based Cyberbullying Detection (2023) · cites this paper as [63]
Some authors perform integration of RNN-CNN-based techniques.
“However, we also observe that some authors [63] perform integration of RNN-CNN-based techniques.”
From A Review on Deep-Learning-Based Cyberbullying Detection (2023) · cites this paper as [63]
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References 14 · Cited by 33
- Authors
- Gada, M., Damania, K., & Sankhe, S.
- ORCID
- Sankhe, Smita
- Venue
- 2021 International Conference on Computer Communication and Informatics (ICCCI)
- Publisher
- IEEE
- Published
- 2021-01-27
- Pages
- 1-6
- OpenAlex
- W3159201688
- Cited by
- 33
- References
- 14
- Citations by year
- 2026: 3 · 2025: 6 · 2024: 10 · 2023: 8 · 2022: 6
- FWCI
- 2.77
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on Computer Communication and Informatics (ICCCI) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Hate Speech and Cyberbullying Detection
- Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
- Physical Sciences › Computer Science › Information Systems › Spam and Phishing Detection
- Keywords
- cyberbullying detection, Word2Vec embeddings, tweets, toxicity classification, Telegram bot, browser extension, WhatsApp
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