Graph convolution networks for social media trolls detection use deep feature extraction

Abstract This study presents a novel approach to identifying trolls and toxic content on social media using deep learning. We developed a machine-learning model capable of detecting toxic images through their embedded text content. Our approach leverages GloVe word embeddings to enhance the model's...

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Главные авторы:	Muhammad Asif, Muna Al-Razgan, Yasser A. Ali, Long Yunrong
Формат:	Artigo
Язык:	Inglês
Опубликовано:	SpringerOpen 2024-02-01
Серии:	Journal of Cloud Computing: Advances, Systems and Applications
Предметы:	Data mining Digital forensics Machine learning Social media Toxic data
Online-ссылка:	https://doi.org/10.1186/s13677-024-00600-4
Метки:	Добавить метку Нет меток, Требуется 1-ая метка записи!

Graph convolution networks for social media trolls detection use deep feature extraction

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