Project Abstract

For content producers and copyright holders, the mass circulation of stolen digital content?��especially via Telegram and other platforms?��has become a serious concern. Telegram has become a hotspot for digital pirates due to its ease of exchanging huge files and anonymity features, which has resulted in considerable financial losses and undermined intellectual property rights. The goal of this research is to create an automated system that can track and identify channels and groups engaged in content piracy on Telegram, as well as detect and monitor the distribution of illegal content.This study attempts to offer an efficient method of preventing digital piracy by utilising machine learning, natural language processing (NLP), and Telegram’s API. This will enable content producers and copyright holders to safeguard their intellectual property rights. The main goal is to develop an automated system that can precisely identify channels and groups sharing pirated content on Telegram, as well as comprehend the underlying patterns and reasons behind these activities.The study will employ data collection techniques like web scraping, API integration, and sampling methods to access piracy groups. Text mining, pattern matching, machine learning classification models, data visualization, and pattern detection methodologies will be utilized to detect pirated content and analyze collected data. Important findings include techniques for reliably detecting stolen multimedia material, characteristics for precise identification, comprehension of user behaviour that encourages piracy, and creation of countermeasures on Telegram.By contributing an automated system to effectively detect pirated content distribution and comprehensively understand associated patterns on Telegram, this research will enable more effective strategies to protect intellectual property while empowering content creators in the digital age.

Keywords: Machine Learning, Natural language processing, Telegram

 Conference Details

Session: Presentation Stream 11 at Presentation Slot 7

Location: College 127 at Tuesday 7th 13:30 – 17:00

Markers: Nicholas Micallef, Alec Critten (GTA)

Course: MSc Data Science, Masters PG

Future Plans: I’m looking for work