Summary
This paper presents an integrated machine learning model for automated cyberbullying detection that combines feature extraction (capturing psychological features, user comments and context) with an artificial neural network classifier enhanced by deep reinforcement learning. The reinforcement learning component provides evaluation through reward/penalty mechanisms to improve classification performance. Simulation results demonstrate that the ANN-DRL approach outperforms conventional machine learning classifiers across standard performance metrics.
UK applicability
This paper is not applicable to UK farming systems, soil health, nutrient density or food-related human health research. It concerns social media content moderation and has no relevance to Vitagri's Pulse Brain catalogue scope.
Key measures
Accuracy, precision, recall, f-measure
Outcomes reported
The study reports classification performance metrics (accuracy, precision, recall, f-measure) of an artificial neural network combined with deep reinforcement learning for detecting cyberbullying in social media text. The proposed ANN-DRL model achieved higher classification results than conventional machine learning classifiers.
Topic tags
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