Blockchain-based rumor detection approach for COVID-19

Rani, Poonam; Jain, Vibha; Shokeen, Jyoti; Balyan, Arnav

doi:10.1007/s12652-022-03900-2

Cited by 9 publications

(2 citation statements)

References 35 publications

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“…As we can see in fig. (5), that our purposed model is outperforming every model and it has 98.7% accuracy almost 1% higher accuracy than the highest accuracy claim by the best algorithm which is 97.8% in this dataset [19]. Our model is more consistent in every epoch which we run while testing it on this dataset.…”

Section: ) Isotmentioning

confidence: 66%

“…Fake news detection is divided into three parts of detection first is textual data, the second is image-related data, and the third is video-related data. There are lots of models used to detect fake news from all of these outcomes like in [19] blockchain and Bi-LSTM is used to achieve the highest accuracy in all of the given research articles for the dataset of PolitiFact, in Gossipcop which is a smaller dataset the CNN approach used by [38] is excellent in all the other research articles. On Twitter election dataset XLM-RoBERTa CNN approach is used in [50] is giving maximum accuracy among all the other models.…”

Section: Related Workmentioning

confidence: 99%

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Fake News Detection System Using Featured-Based Optimized MSVM Classification

et al. 2022

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Fake News creates erroneous suspense information that can be identified. This spreads dishonesty about a country's status or overstates the expense of special functions for a government, destroying democracy in certain countries, such as in the Arab Spring. Associations such as the ''House of Commons and the Crosscheck project'' address concerns such as publisher responsibility. However, since they rely entirely on manual detection by humans, their coverage is minimal. This is neither sustainable nor possible in a world where billions of items are withdrawn or posted every second. The paper produces a deep study on past research work on fake news detection on the selected data-sets and proposes an algorithm with Multi-layered Principal Component Analysis for feature selection followed by firefly-optimized algorithm. Multi-Support Vector Machines(MSVM) are finally used to classify the news. We used ten different data-sets for testing the proposed algorithm. As the number of features in the data-sets are more, feature extration and selection methods help to improve the accuracy in respective data-sets. Only the datasets having less number of features gave a lower performance on our feature extraction algorithms. INDEX TERMSFake news, rirefly, long short-term memory (LSTM), multi support vector machine (MSVM).

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Section: ) Isotmentioning

confidence: 66%