BERT and CNN for automated detection of detrimental discourse
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Date
2026-02-19
Journal Title
Journal ISSN
Volume Title
Publisher
IEE
Abstract
The modern online age is competitive and full of information that requires sensing dangerous materials in order to maintain online honesty and facilitate positive forms of communication. This paper presents a new model that takes a hybrid approach to using BERT Base embeddings and convolutional neural networks (CNNs) to deal with the problem of detecting harmful material in a variety of settings. BERT Base embeddings are used to dynamically gain the fined tuned features of semantics of text that is then processed by a CNN to classify. Compared to the previous studies mainly concentrated on individual contexts with LSTM, RNN, or more advanced transformer models, we use the effectiveness of CNNs to recognize patterns in contexts and also work with different types of harmful content at the same time. To evaluate it, we applied benchmark datasets a reputable Mendeley dataset of hate speech detection, as well as Kaggle datasets of cyberbullying and emotional distress. The experimental results point to the power of our method, which reached almost 92 percent of accuracy in categories. This study merges semantic expressiveness and CNN effectiveness and contributes to content moderation policies and meaningful insights to create safer and more inclusive websites.
Description
Through integrating indigenous and scientific agricultural knowledge, the research seeks to strengthen extension services, improve farmers’ productivity and livelihoods, and support innovation, agricultural modernization, and sustainable development in Uganda.
Keywords
BERT, CNN, Detrimental Content, Multi Context, Hate Speech, Cyberbullying, Emotional Turmoil
Citation
Devarapalli, T., Yarlagadda, N., Sivaramakrishnan, A., Haldorai, A., Ali, G., & Sharma, V. (2025, November). BERT and CNN for automated detection of detrimental discourse. In 2025 5th International Conference on Advancement in Electronics & Communication Engineering (AECE) (pp. 196-201). IEEE.