SOURCE-LINKED INTELLIGENCE

Responsive classifiers against hate speech in low-resource settings

CORDIS · observation · Publication date unknown

"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 model

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recordType

award

status

SIGNED

region

EU

value

150000

unit

EUR