Innovating Sampling Technique with Distil Roberta Neural Network for Unhealthy Conversation Detection Through Twitter - Girishkumar, Shah Hemal; Joshi, Dr. Hiren (Discontinuity, Nonlinearity, and Complexity, 2026)
Detecting unhealthy conversations online presents significant challenges, especially regarding class imbalance and the nuanced features of social media language. Class imbalance can lead to biased models and poor performance, particularly in identifying minority class instances. Furthermore, existing methods often struggle to accurately detect unhealthy conversations due to the complexity of language nuances and the sheer volume of online discourse. To address these challenges, this paper presents the Stratified RoBERTa Enhanced Framework for detecting unhealthy conversations online. This framework employs stratified sampling during data pre-processing to ensure proper distribution and preservation of minority classes, effectively mitigating the negative impact of class imbalance. Additionally, we introduce a novel technique using the Hugging Face Auto Tokenizer to enhance tokenisation efficiency. The proposed approach utilizes a neural network architecture that integrates a pre-trained DistilRoBERTa-base model, followed by a hidden layer with ReLU activation. Fine-tuning with the Adam optimizer further enhances the model’s adaptability to varying learning rates. Experimental results, illustrated through Receiver Operating Characteristic (ROC) graphs, demonstrate improved true positive rates and false positive rates, affirming the efficacy of the proposed framework in accurately detecting unhealthy conversations.