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Averting and Countering Online Hatred.
With a performance of 75% accuracy, the results show that the transliteration strategy outperforms both raw and translated data by…
The suggested approach uses shared representation knowledge across five Arabic dialects—Egyptian, Saudi, Levant, Gulf, and Algerian—and is intended to detect…
The study offers a thorough examination of the many types of abuse that are common on social media, with an…
Browse and read from a list of the 165 most popular LinkedIn resources dealing with soft counter-extremism, countering hate speech,…
In order to investigate the prospect of employing machine translation to create a parallel multilingual hate speech dataset, the researchers…
Two multimodal hate speech datasets, MHS and MHS-Con, which record fine-grained hostile abstractions in everyday and confusing situations, are curated…
Browse and read from a list of the 148 most popular LinkedIn resources dealing with soft counter-extremism, countering hate speech,…
The multimodal character of digital information makes it even more difficult to moderate hate speech (HS) in the dynamic online…
The authors use text embeddings from computational linguistics to develop and evaluate a approach for quantifying the distortions caused by…
To stop hatred and nasty language from spreading online, automatic detection is crucial. By identifying and elucidating hate speech, we…
In order to produce CNs with a high level of specificity for invisible targets, the authors suggest Retrieval-Augmented Zero-shot Generation…
The paper presents CoARL, a unique framework that models the pragmatic consequences of social biases in hostile remarks, hence improving…