Project description
Detection of unwanted internet behaviour powered by machine learning
Cyber grooming, toxicity, harassment and bullying on online platforms pose significant threats to young people. Researchers from the Norwegian University of Science and Technology (NTNU) and spin-off company AIBA have developed machine learning (ML) models that use text and keystroke dynamics to detect harmful behaviour early and reduce harm and toxicity on their platforms. The EU-funded Aiba project aims to provide safe digital solutions for kids and teenagers. It incorporates real-time ML-powered detection of fake profiles in games and social media. This moderation service evaluates all ongoing conversations in real time on gaming and social media platforms, pinpointing grooming discussions in under 20 messages. This not only simplifies moderation but also aids customers in preventing harmful behaviour, ultimately saving a substantial number of human hours.
Objective
Aiba creates safe digital lives for kids and teenagers. We do real-time detection of fake profiles and unwanted behavior, such as cyber grooming, toxicity, harassment and bullying in games and social media. Our machine learning powered moderation service will make moderation easier for our customers. We risk score all ongoing conversations real-time on gaming and social media platforms, and help our customers to prevent unwanted behavior before it makes any harm. We help them stop cyber grooming early and our initial results show we can detect grooming coversations in less than 20 messages .Our service is powering our customers with increased community health and more efficient moderation through proactive and continuous risk scoring. They can save huge amounts of human hours by using our solution. Our unique SaaS multimodal approach, combined with machine learning makes for a new era in chatroom moderation.
With a novel approach based on award-winning research, researchers and data scientists at Aiba and NTNU, have developed and trained machine learning models with text and keystroke dynamics to detect cyber grooming and other unwanted behavior early so they can stop conversations to minimize harm and toxicity on their platforms. The solution has been trained and tested on short texts typically sent in a chat. From this minimal amount of information, the system has accurately been able to profile the chatter and determine both their age group and gender. The solution has been further developed to perform a continuous real-time analysis of the text messages, to mark suspicious conversations. To do this, natural language processing features (turning text into numerical feature vectors) are used, in combination with machine learning techniques.
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Keywords
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Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Programme(s)
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
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HORIZON.3.2 - European innovation ecosystems
MAIN PROGRAMME
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Topic(s)
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Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Funding Scheme
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Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
HORIZON-CSA - HORIZON Coordination and Support Actions
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Call for proposal
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Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
(opens in new window) HORIZON-EIE-2022-SCALEUP-02
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Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.
2815 Gjovik
Norway
The organization defined itself as SME (small and medium-sized enterprise) at the time the Grant Agreement was signed.
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