Graph convolution networks for social media trolls detection use deep feature extraction
Graph convolution networks for social media trolls detection use deep feature extraction
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Publisher
Berlin/Heidelberg: Springer Berlin Heidelberg
Journal title
Language
English
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Publisher
Berlin/Heidelberg: Springer Berlin Heidelberg
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Scope and Contents
Contents
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 predictive accuracy. We also utilized Graph Convolutional N...
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Full title
Graph convolution networks for social media trolls detection use deep feature extraction
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TN_cdi_doaj_primary_oai_doaj_org_article_127b56f0458341ee8cc5e5dfa75cedb0
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_127b56f0458341ee8cc5e5dfa75cedb0
Other Identifiers
ISSN
2192-113X
E-ISSN
2192-113X
DOI
10.1186/s13677-024-00600-4
