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- W4385434980 abstract "Though social media have emerged as a convenient platform for news consumption and update about various events, the spread of fake news over social media is a threat to the freedom of expression and democracy. In the literature, studies related to fake news detection on social media can be categorized into two categories: tweet propagation networks with a user characteristic-based and tweet propagation networks with post and reply content. Tweet propagation network with a user characteristic-based model suffers from a lack of user profile information available due to users’ privacy concerns. Tweet propagation networks with post and reply content are language-dependent. Motivated by the above two challenges, this study proposes a centrality-aware graph convolution network CGCN model. CGCN model incorporates the centrality of every node to overcome the limitation of user profile information and not rely on the text in posts and replies to make it language-independent. Our experimental results of two benchmark datasets suggest that the proposed model efficiently detects fake news without using text information in posts and comments. Hence, our proposed model can be used to detect fake news in any vernacular language." @default.
- W4385434980 created "2023-08-01" @default.
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- W4385434980 date "2023-01-01" @default.
- W4385434980 modified "2023-10-16" @default.
- W4385434980 title "Language-Independent Fake News Detection over Social Media Networks Using the Centrality-Aware Graph Convolution Network" @default.
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- W4385434980 doi "https://doi.org/10.1007/978-981-99-3080-7_7" @default.
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