Year: 2026
Type: article
Abstract: The illegal sale of drugs using social media is a matter of increasing concern among governments. This study proposes a deep-learning-based method to monitor such activities and identify drug-related content using an intelligent Telegram-based chatbot system. The proposed framework uses NLP to analyse textual messages and CNNs to detect drug-related images. Data collection is performed using Telegram APIs, followed by preprocessing steps to ensure data quality. The system uses a BERT-based model to classify suspicious messages and generate alerts automatically. Additionally, CNN models analyse multimedia posts containing drug-related images. Experimental results show that combining the detection of text and images significantly enhances overall accuracy and recall. Overall, the system monitors and identifies drug-related activities on Telegram in real time and demonstrates how deep-learning-based chatbot technology can support law enforcement agencies in enhancing online safety.
Authors: Pushparani S, Vidya Sri B, Sai Shireesh B
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7127941702
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