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Real-time Early Detection and Alert System for Online Terrorist Content based on Natural Language Processing, Social Network Analysis, Artificial Intelligence and Complex Event Processing

Project description

DEENESFRITPL

Latest technology employed in fight against terrorism

Terrorism is a growing threat to global security, and traditional methods of combating it are proving increasingly ineffective. The challenge for law enforcement agencies is how to gather, process and analyse vast amounts of online data related to terrorist groups quickly and efficiently. But with social media platforms flooded with unstructured data in multiple languages, this task is ever more daunting. In response, the EU-funded RED-Alert project will bring together data mining and predictive analytics tools with cutting-edge technologies such as natural language processing, semantic media analysis, social network analysis, complex event processing and artificial intelligence. Backed by Europol, the project’s aim is to enable law enforcement agencies to collect, process, visualise and store online data related to terrorist groups.

Objective

The RED-Alert project will bring data mining and predictive analytics tools to the next level, developing novel natural language processing (NLP), semantic media analysis (SMA), social network analysis (SNA), Complex Event Processing (CEP) and artificial intelligence (AI) technologies. These technologies will be combined for the first time and validated by 6 law enforcement agencies (LEAs) to collect, process, visualize and store online data related to terrorist groups, allowing them to take coordinated action in real-time while preserving the privacy of citizens.
The RED-Alert solution will outperform state-of-the-art solutions in terms of number of languages supported, privacy-preserving capabilities, usability, detection performance, real-time capabilities and integration capabilities. The RED-Alert approach combines for the first time the CEP methodology with NLP/SMA and SNA applications in the context of social media data analytics, transforming (unstructured) social media data into (structured) events enhanced by semantic attributes. For example, a tweet will be an event consisting of content (expressed as NLP features e.g. concepts, sentiment, entities, etc.) and context (time and the author including SNA features e.g. number of followers, number of links, etc.). Turning unstructured social media data into structured events is key, as it allows the system to use (event) rules (event temporal logic, event logic patterns, even counting, absence of events) to infer insights or create alerts in real-time.
The project impact is supported by the participation of Europol and specific dissemination activities around the World Counter-Terrorism Summit, organized by one of the partners. The total requested EC funding is 5M Euros and the project duration 36 months.

Fields of science

Programme(s)

Topic(s)

Call for proposal

H2020-SEC-2016-2017

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Sub call

H2020-SEC-2016-2017-1

Funding Scheme

RIA - Research and Innovation action

Coordinator

SOFTWARE IMAGINATION & VISION SRL
Net EU contribution
€ 209 173,75
Address
SOSEAUA BUCURESTI-PLOIESTI 73-81 COMPLEX VICTORIA CORP CLADIRE C4 ETAJ 2
013685 Bucuresti
Romania

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Region
Macroregiunea Trei Bucureşti-Ilfov Bucureşti
Activity type
Private for-profit entities (excluding Higher or Secondary Education Establishments)
Links
Total cost
€ 209 173,75

Participants (17)

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Last update: 23 August 2022

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Permalink: https://cordis.europa.eu/project/id/740688

European Union, 2024