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Erschienen in:
Network Design Through Graph Neural Networks: Identifying Challenges and Improving Performance Kapitel lesen Erstes Kapitel lesen

2024 | OriginalPaper | Buchkapitel

FakEDAMR: Fake News Detection Using Abstract Meaning Representation Network

verfasst von : Shubham Gupta, Narendra Yadav, Suman Kundu, Sainathreddy Sankepally

Erschienen in: Complex Networks & Their Applications XII

Verlag: Springer Nature Switzerland

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Abstract

Given the rising prevalence of disinformation and fake news online, the detection of fake news in social media posts has become an essential task in the field of social network analysis and NLP. In this paper, we propose a fake detection model named, FakEDAMR that encodes textual content using the Abstract Meaning Representation (AMR) graph, a semantic representation of natural language that captures the underlying meaning of a sentence. The graphical representation of textual content holds longer relation dependency in very few distances. A new fake news dataset, FauxNSA, has been created using tweets from the Twitter platform related to ‘Nupur Sharma’ and ‘Agniveer’ political controversy. We embed each sentence of the tweet using an AMR graph and then use this in combination with textual features to classify fake news. Experimental results on publicly and proposed datasets with two different sets show that adding AMR graph features improves F1-score and accuracy significantly. (Code and Dataset: https://​github.​com/​shubhamgpt007/​FakedAMR)

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Vorheriges Kapitel Empirical Study of Graph Spectra and Their Limitations
Nächstes Kapitel Visual Mesh Quality Assessment Using Weighted Network Representation
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Metadaten
Titel
FakEDAMR: Fake News Detection Using Abstract Meaning Representation Network
verfasst von
Shubham Gupta
Narendra Yadav
Suman Kundu
Sainathreddy Sankepally
Copyright-Jahr
2024
Buch
Complex Networks & Their Applications XII

Print ISBN: 978-3-031-53467-6

Electronic ISBN: 978-3-031-53468-3

Copyright-Jahr: 2024

DOI
https://doi.org/10.1007/978-3-031-53468-3_26

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