Source proof for Bodyguard builds a machine-learning model on OVHcloud AutoML to detect hateful comments, reaching 90% detection with a 3% false-positive rate

…in Send by email Download casestudy-bodyguard-ovhcloud-en-0120_1 (PDF /4.83 MB) 90% of hateful comments detected by the application 2% error margin for the algorithm (false positives) 2 million+ hateful comments…

…accuracy of Bodyguard technology has increased by 10%, moving from 80% to 90%. The number of false positives has also decreased by 15%, from 6% to 3%. In terms of monitoring, Charles chose to use the Logs Data Platform (currently…

…detected by the application 2% error margin for the algorithm (false positives) 2 million+ hateful comments deleted in 20 months The context Charles Cohen started learning about computer programming when he w…

…se models, the detection accuracy of Bodyguard technology has increased by 10%, moving from 80% to 90%. The number of false positives has also decreased by 15%, from 6% to 3%. In ter…

…online or expose myself on public platforms — I was fearful of being bullied.” Charles Cohen, founder and CEO of Bodyguard The challenge The technology needed to be capable of analysing the context in w…

Intelligent protection against cyber-bullying | OVHcloud Worldwide Open menu Webmail Notifications Back to menu No pending or…

…ul of being bullied.” Charles Cohen, founder and CEO of Bodyguard The challenge The technology needed to be capable of analysing the context in which a comment is made, and determining the person or people it is aimed at. Bodyguard technology had to be able to understand and interpret states of mind.…

…machine learning . In terms of the specifications, it was a matter of accuracy. The predictive model could not exceed an error rate of 10%. The solution A managed, easy-to-use service that could accelerate the productio…

…deleted in real time, the number of API requests, and more. The hardware layer The Bodyguard infrastructure is made up of three Public Cloud instances: one for managing the databases another for the technology and machine learning…

…opment phase. 10 days were needed to create the Bodyguard predictive model, and it took 20 days to design the meta-learning model, which analyses the relationship between the author of the content and the commenter. With these models, the detection accuracy of Bodyguard technology has increase…

…hateful comments in real time on YouTube, Instagram, Twitter, Twitch and Mixer. In July 2019, this virtual bodyguard gathered a following of more than 40,000 users, and boasted a satisfaction score of 97%. Here are a few reasons why it is so s…

…detected by the application 2% error margin for the algorithm (false positives) 2 million+ hateful comments deleted in 20 months The context Charles Cohen started learning about computer programming when he w…

…hnology also needed to be capable of detecting irony, sarcasm, and even humour. The predictive model created with the OVHcloud AutoML machine learning platform really helped me achieve this.” Charles Cohen, founder and CEO of Bodyguard The predictive model also needed t…

…ocess for requesting machine learning models. He could also use it to integrate open-source algorithms, such as those offered by scikit-learn. OVHcloud AutoML also accelerated the development phase. 10 days were needed to…

…y-to-use service that could accelerate the production phase. The software layer Charles chose to use OVHcloud AutoML , a distributed and scalable machine learning platform. With this Software-as-a…

…ionship between the author of the content and the commenter. With these models, the detection accuracy of Bodyguard technology has increased by 10%, moving from 80% to 90%. The number of false positives has also decreased by 15%, from 6% to 3%. In terms of monitoring, Charles chose to use the Logs Data Platform (currently…