Cargando…
Augmentation and heterogeneous graph neural network for AAAI2021-COVID-19 fake news detection
Misinformation has become a frightening specter of society, especially fake news that concerning Covid-19. It massively spreads on the Internet, and then induces misunderstandings of information to the national and global communities during the pandemic. Detecting massive misinformation on the Inter...
Autores principales: | Karnyoto, Andrea Stevens, Sun, Chengjie, Liu, Bingquan, Wang, Xiaolong |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8742573/ https://www.ncbi.nlm.nih.gov/pubmed/35035595 http://dx.doi.org/10.1007/s13042-021-01503-5 |
Ejemplares similares
-
Fake news detection: A survey of graph neural network methods
por: Phan, Huyen Trang, et al.
Publicado: (2023) -
Sentiment Analysis for Fake News Detection by Means of Neural Networks
por: Kula, Sebastian, et al.
Publicado: (2020) -
EchoFakeD: improving fake news detection in social media with an efficient deep neural network
por: Kaliyar, Rohit Kumar, et al.
Publicado: (2021) -
Intra-graph and Inter-graph joint information propagation network with third-order text graph tensor for fake news detection
por: Cui, Benkuan, et al.
Publicado: (2023) -
Practice Notes from the AAAI
Publicado: (2020)