Title: Retrieval and Analysis of Heterogeneous Online Content for Terrorist Activity Recognition
Grant agreement no: 700024
Duration: 1 September 2016 – 30 November 2019
Law Enforcement Agencies (LEAs) across Europe face today important challenges in how they identify, gather and interpret terrorist generated content online. The Dark Web presents additional challenges due to its inaccessibility and the fact that undetected material can contribute to the advancement of terrorist violence and radicalisation. LEAs also face the challenge of extracting and summarising meaningful and relevant content hidden in huge amounts of online data to inform their resource deployment and investigations. In this context, the main objective of the TENSOR project is to provide a powerful terrorism intelligence platform offering LEAs fast and reliable planning and prevention functionalities for the early detection of terrorist organised activities, radicalisation and recruitment.
The platform integrates a set of automated and semi-automated tools for efficient and effective searching, crawling, monitoring and gathering online terrorist-generated content from the Surface and the Dark Web; Internet penetration through intelligent dialogue-empowered bots; Information extraction from multimedia (e.g., video, images, audio) and multilingual content; Content categorisation, filtering and analysis; Real-time relevant content summarisation and visualisation; Creation of automated audit trails; Privacy-by-design and data protection. The project brings together industry, LEAs, legal experts and research institutions. It is expected that this collaboration will have significant impact on:
- ensuring the final system meets end-user LEA requirements,
- enabling LEAs to access and examine terrorist generated content online bringing significant advantages to their operational capability, and
- promoting industry’s enhanced understanding of operational LEA requirements and their market competitiveness in the field of online organised crime, terrorism and harmful-radicalisation.
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 700024