CLASSIFICATION OF FAKE NEWS UTILISING TENSOR  DECOMPOSITION AND GRAPH CONVOLUTIONAL NETWORK

Mrs.K.RAJANI , KONGARA SINDHU, JOGU SHRUTHI, MERUGU PRANAVI

CLASSIFICATION OF FAKE NEWS UTILISING TENSOR DECOMPOSITION AND GRAPH CONVOLUTIONAL NETWORK

Authors

Mrs.K.RAJANI , KONGARA SINDHU, JOGU SHRUTHI, MERUGU PRANAVI

Keywords:

Fake news, Social media, Sentence interaction patterns, Graph representation, Graph neural network (GNN), Local word co-occurrence, Third-order co-occurrence tensor, Canonical polyadic (CP) decomposition, Contextual information, Multiclass classification

Abstract

With the wide spread of fake news onsocial media, its impact has become a major concernof the public, so accurate detection methods areurgently needed. However, these methods rarely

References

N. Grinberg, K. Joseph, L. Friedland, B. SwireThompson, and D. Lazer, “Fake news on Twitter during the 2016 US presidential election,” Science, vol. 363, no. 6425, pp. 374–378, 2019.

A. Bondielli and F. Marcelloni, “A survey on fake news and rumour detection techniques,” Inf. Sci., vol. 497, pp. 38–55, Sep. 2019.

Downloads

Published

2024-02-15

How to Cite

Mrs.K.RAJANI , KONGARA SINDHU, JOGU SHRUTHI, MERUGU PRANAVI. (2024). CLASSIFICATION OF FAKE NEWS UTILISING TENSOR DECOMPOSITION AND GRAPH CONVOLUTIONAL NETWORK. Journal of Computational Analysis and Applications (JoCAAA), 32(2), 339–348. Retrieved from https://www.eudoxuspress.com/index.php/pub/article/view/2708