Respond2Hate
Financiado
Responsive classifiers against hate speech in low-resource settings
"Hate speech is a worldwide phenomenon that is increasingly pervading online spaces, creating an unsafe environment for users. While tech companies address this problem by server-side filtering using machine learning models traine...
"Hate speech is a worldwide phenomenon that is increasingly pervading online spaces, creating an unsafe environment for users. While tech companies address this problem by server-side filtering using machine learning models trained on large datasets, these automatic methods cannot be applied to most languages due to lack of available training data.
Based on recent results of the PI's ERC project on multilingual representation models in low-resource settings, Respond2Hate aims at developing a pilot browser extension that allows users to locally remove hateful content from their social media feeds themselves, without having to rely on the support of tech companies.
Since hate speech is highly dependent on cultural context, responsive classifiers are needed that adapt to the individual environment. Commercial efforts focus on large-scale, general-purpose models which are often burdened with representation and bias problems, and therefore cope poorly with swiftly changing targets or information shift between regional contexts. In contrast, we seek to develop lightweight, adaptive models that require only a small dataset for initial fine-tuning by continuously enhancing model capabilities over time.
This is achieved by applying state-of-the-art Natural Language Processing (NLP) and deep learning techniques for pre-trained language models like low-resource transfer of hate speech representations from high-resource languages and few-shot learning based on limited user feedback. We have already successfully applied these methods in low-resource multilingual settings, and will now validate their use for hate speech filtering.
By making hate speech detection and reduction available in ""low-resource"" countries with little representation in current training datasets, which are currently not served well by governments, industry and NGOs, Respond2Hate will empower users to self-control their exposure to hate speech, fostering a healthier and safer online environment."
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Duración del proyecto: 22 meses
Fecha Inicio: 2023-06-16
Fecha Fin: 2025-04-30
Fecha Fin: 2025-04-30
Línea de financiación: concedida
El organismo HORIZON EUROPE notifico la concesión del proyecto el día 2023-06-16
Línea de financiación objetivo
El proyecto se financió a través de la siguiente ayuda:
ERC-2022-POC2:
ERC PROOF OF CONCEPT GRANTS2
Cerrada
hace 2 años
Presupuesto
El presupuesto total del proyecto asciende a
150K€
Líder del proyecto
LUDWIGMAXIMILIANSUNIVERSITAET MUENCHEN
No se ha especificado una descripción o un objeto social para esta compañía.
Perfil tecnológico
TRL
4-5
Perfil tecnológico
TRL
4-5