#3345. Improving fake news detection with domain-adversarial and graph-attention neural network

November 2026publication date
Proposal available till 30-06-2025
4 total number of authors per manuscript5500 $

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Journal’s subject area:
Arts and Humanities (miscellaneous);
Developmental and Educational Psychology;
Management Information Systems;
Information Systems;
Information Systems and Management;
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More details about the manuscript: Science Citation Index Expanded or/and Social Sciences Citation Index
Abstract:
With the widespread use of online social media, we have witnessed that fake news causes enormous distress and inconvenience to peoples social life. Although previous studies have proposed rich machine learning methods for identifying fake news in social media, the task of detecting fake news in emerging news events/domains remains a challenging problem due to the wide range of news topics on social media as well as the evolution and variation of fake news contents in the web. Its main advantage is that, in a text environment with multiple events and domains, only partial domain sample data are needed to train the model to achieve accurate cross-domain fake news detection in those domains with few samples. Extensive experiments were conducted on two multimedia datasets of Twitter and Weibo, and the results showed that the proposed model was very effective in detecting fake news across events.
Keywords:
Adversarial neural network; Fake news detection; Feature extraction; Graph-attention network

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