A probabilistic approach toward evaluation of Internet rumor on COVID

Yang, Yancheng; Nazir, Shah; Khalil, Wajeeha

doi:10.1007/s00500-022-07064-1

Cited by 8 publications

(3 citation statements)

References 45 publications

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“…2. On the other hand, the CHECKED Dataset has been employed by [27] in their analysis of extended text feature extraction networks with data augmentation (LTFE) and other research papers [28], [29], [30]. ISOT Fake News Dataset has 21417 real news and 23481 fake news, with a total of 44898 news.…”

Section: A Multi-tier Fake News Detection Modelmentioning

confidence: 99%

A Multi-tier Model and Filtering Approach to Detect Fake News Using Machine Learning Algorithms

Chang Yu,

A Hamid,

Abdullah

et al. 2024

JOIV : Int. J. Inform. Visualization

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Fake news trends have overgrown in our societies over the years through social media platforms. The goal of spreading fake news can easily mislead and manipulate the public’s opinion. Many previous researchers have proposed this domain using classification algorithms or deep learning techniques. However, machine learning algorithms still suffer from high margin error, which makes them unreliable as every algorithm uses a different way of prediction. Deep learning requires high computation power and a large dataset to operate the classification model. A filtering model with a consensus layer in a multi-tier model is introduced in this research paper. The multi-tier model filters the news label correctly predicted by the first two-tier layer. The consensus layer acts as the final decision when collision results occur in the first two-tier layer. The proposed model is applied to the WEKA software tool to test and evaluate the model from both datasets. Two sequences of classification models are used in this research paper: LR_DT_RF and LR_NB_AdaBoost. The best performance of sequence for both datasets is LR_DT_RF which yields 0.9892 F1-Score, 0.9895 Accuracy, and 0.9790 Matthews Correlation Coefficient (MCC) for ISOT Fake News Dataset, and 0.9913 F1-Score, 0.9853 Accuracy, and 0.9455 MCC for CHECKED Dataset. This research could give researchers an approach for fake news detection on different social platforms and feature-based

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Section: A Multi-tier Fake News Detection Modelmentioning

confidence: 99%

A Multi-tier Model and Filtering Approach to Detect Fake News Using Machine Learning Algorithms

Chang Yu,

A Hamid,

Abdullah

et al. 2024

JOIV : Int. J. Inform. Visualization

View full text Add to dashboard Cite

show abstract

“…Referencing the idea of Jia et al [31] and Huo et al [32], we assume that the white noise is stochastic perturbations as the primary reason for the system variable. Based on the aforementioned discussion, we add white noise to obtain a stochastic analog of the deterministic model (1) with periodic coefficients as follows:…”

Section: The Stochastic Sicr Rumor Model Incorporating Media Coveragementioning

confidence: 99%

“…Rumor, a specific type of misinformation, is information whose integrity has not been verified and spread to cause an impact [1]. Spreading rumors can occasionally be advantageous; for instance, we might use their quick and effective nature to alert the public and urge them to take the necessary precautions [2].…”

Section: Introductionmentioning

confidence: 99%

Dynamical behavior of a stochastic SICR rumor model incorporating media coverage

Yue

Huo²

2022

Front. Phys.

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Rumor propagation in the new media era poses a huge threat to maintaining the normal order of social life. In this context, we put forth a nonlinear dynamics-based stochastic SICR rumor model that integrates media coverage with science education. First, the existence of a unique global positive solution is obtained. Second, sufficient conditions for extinction are constructed on the spread of rumors based on the Lyapunov function methods and Khasminskii’s theory. Finally, the theoretical analysis is verified through numerical simulations. Additionally, it demonstrates how rumor spreading can be hampered by media coverage.

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Evaluation of the absolute forms of cost functions in optimization using a novel evolutionary algorithm

Mohammadi,

Nariman-zadeh,

Payan

et al. 2023

Soft Comput

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A probabilistic approach toward evaluation of Internet rumor on COVID

Cited by 8 publications

References 45 publications

A Multi-tier Model and Filtering Approach to Detect Fake News Using Machine Learning Algorithms

A Multi-tier Model and Filtering Approach to Detect Fake News Using Machine Learning Algorithms

Dynamical behavior of a stochastic SICR rumor model incorporating media coverage

Evaluation of the absolute forms of cost functions in optimization using a novel evolutionary algorithm

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