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Rumor Detection Based on SAGNN: Simplified Aggregation Graph Neural Networks
- Source :
- Machine Learning and Knowledge Extraction, Volume 3, Issue 1, Pages 5-94, Machine Learning and Knowledge Extraction, Vol 3, Iss 5, Pp 84-94 (2021)
- Publication Year :
- 2020
- Publisher :
- MDPI AG, 2020.
-
Abstract
- Identifying fake news on the media has been an important issue. This is especially true considering the wide spread of rumors on the popular social networks such as Twitter. Various kinds of techniques have been proposed for automatic rumor detection. In this work, we study the application of graph neural networks for rumor classification at a lower level, instead of applying existing neural network architectures to detect rumors. The responses to true rumors and false rumors display distinct characteristics. This suggests that it is essential to capture such interactions in an effective manner for a deep learning network to achieve better rumor detection performance. To this end we present a simplified aggregation graph neural network architecture. Experiments on publicly available Twitter datasets demonstrate that the proposed network has performance on a par with or even better than that of state-of-the-art graph convolutional networks, while significantly reducing the computational complexity.
- Subjects :
- lcsh:Computer engineering. Computer hardware
Computational complexity theory
Graph neural networks
Computer science
graph neural network
lcsh:TK7885-7895
02 engineering and technology
Machine learning
computer.software_genre
020204 information systems
TheoryofComputation_ANALYSISOFALGORITHMSANDPROBLEMCOMPLEXITY
0202 electrical engineering, electronic engineering, information engineering
automotive_engineering
Artificial neural network
business.industry
Deep learning
rumor detection
Rumor
artificial intelligence
algebra_number_theory
Graph (abstract data type)
Detection performance
020201 artificial intelligence & image processing
Fake news
Artificial intelligence
business
computer
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- Machine Learning and Knowledge Extraction, Volume 3, Issue 1, Pages 5-94, Machine Learning and Knowledge Extraction, Vol 3, Iss 5, Pp 84-94 (2021)
- Accession number :
- edsair.doi.dedup.....36d53fe9c96322561890bdc7f574051d