TL;DR
Two models are proposed: Hing_MTL - a Multi-task Learning (MTL) model implemented by ensembling two pre-trained Hinglish Bidirectional Encoder Representations from Transformers (Hinglish-BERT), and Ensemble_MTL - a MTL model implemented by ensembling two pre-trained models to detect fake and hate content and identify the target and severity of hateful speech.
This work proposes a novel computational methodology and app, the Hate Mitigation App (HMApp), that discovers emerging coded or non-coded hate speech terminology on social media using a set of researcher-selected anchor hate terms to identify new terminology associated with a specific type of hatred.
Social media platforms are key spaces for sharing opinions, experiences, and daily events. However, the rapid rise in hate speech, especially implicit hate, has become a significant concern. Implicit hate is subtle, context-dependent, and more difficult to detect than explicit hate. Despite the growing body of work on...
The experimental results indicate that hierarchical attention mechanisms are effective for multilingual fake news detection, particularly in low-resource multilingual settings.
A multi-agent framework in which a story agent, an image agent, and a critic agent collaborate to produce fake social media posts that plausibly counter true news is introduced, providing a foundation for developing robust defenses against social media fake news.
The use of social media in Nigeria for communication, information access, and public discussion cannot be overemphasized. However, some individuals and organizations exploit it negatively, spreading hate speech, misinformation, and toxic content. Such abusive online behavior has led to politically, socially, and eth...
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.
