An Evolutionary Fake News Detection Method for COVID-19 Pandemic Information

Al‐Ahmad, Bilal; Al-Zoubi, Ala’ M.; Khurma, Ruba Abu; Aljarah, Ibrahim

doi:10.3390/sym13061091

Cited by 81 publications

(37 citation statements)

References 45 publications

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“…Al-Ahmad et al [ 116 ], suggested a powerful approach for detecting misleading news. This approach employs kNN–BSSA, kNN–BPSO, kNN–BGA with feature selection, and the standard kNN models to reduce the number of symmetrical features.…”

Section: Literature Reviewmentioning

confidence: 99%

A survey of uncover misleading and cyberbullying on social media for public health

et al. 2022

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Misleading health information is a critical phenomenon in our modern life due to advance in technology. In fact, social media facilitated the dissemination of information, and as a result, misinformation spread rapidly, cheaply, and successfully. Fake health information can have a significant effect on human behavior and attitudes. This survey presents the current works developed for misleading information detection (MLID) in health fields based on machine learning and deep learning techniques and introduces a detailed discussion of the main phases of the generic adopted approach for MLID. In addition, we highlight the benchmarking datasets and the most used metrics to evaluate the performance of MLID algorithms are discussed and finally, a deep investigation of the limitations and drawbacks of the current progressing technologies in various research directions is provided to help the researchers to use the most proper methods in this emerging task of MLID.

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Section: Literature Reviewmentioning

confidence: 99%

A survey of uncover misleading and cyberbullying on social media for public health

et al. 2022

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“…The most effective way to obtain such an estimate is numerical analysis based on random search for optimal parameters of the management strategy. One of the implementations of random search is evolutionary modeling, which has found wide application in numerical optimization problems [23][24][25][26][27][28].…”

Section: Resultsmentioning

confidence: 99%

Statistical Analysis of Current Financial Instrument Quotes in the Conditions of Market Chaos

2022

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In this paper, the problem of estimating the current value of financial instruments using multidimensional statistical analysis is considered. The research considers various approaches to constructing regression computational schemes using quotes of financial instruments correlated to the data as regressors. An essential feature of the problem is the chaotic nature of its observation series, which is due to the instability of the probabilistic structure of the initial data. These conditions invalidate the constraints under which traditional statistical estimates remain non-biased and effective. Violation of experiment repeatability requirements obstructs the use of the conventional data averaging approach. In this case, numeric experiments become the main method for investigating the efficiency of forecasting and analysis algorithms of observation series. The empirical approach does not provide guaranteed results. However, it can be used to build sufficiently effective rational strategies for managing trading operations.

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“…Fake news starting with shocking headlines can lead to serious results since infections, in particular, are directly associated with human lives [13]. Research on detecting fake news on COVID-19 is actively conducted overseas since there is an ample amount of usable data amid the COVID-19 outbreak [14,15]. However, research is less actively conducted in Korea due to a lack of useful data.…”

Section: Introductionmentioning

confidence: 99%

Fake Sentence Detection Based on Transfer Learning: Applying to Korean COVID-19 Fake News

Lee

Kim

2022

Applied Sciences

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With the increasing number of social media users in recent years, news in various fields, such as politics, economics, and so on, can be easily accessed by users. However, most news spread through social networks including Twitter, Facebook, and Instagram has unknown sources, thus having a significant impact on news consumers. Fake news on COVID-19, which is affecting the global population, is propagating quickly and causes social disorder. Thus, a lot of research is being conducted on the detection of fake news on COVID-19 but is facing the problem of a lack of datasets. In order to alleviate the problem, we built a dataset on COVID-19 fake news from fact-checking websites in Korea and propose deep learning for detecting fake news on COVID-19 using the datasets. The proposed model is pre-trained with large-scale data and then performs transfer learning through a BiLSTM model. Moreover, we propose a method for initializing the hidden and cell states of the BiLSTM model to a [CLS] token instead of a zero vector. Through experiments, the proposed model showed that the accuracy is 78.8%, which was improved by 8% compared with the linear model as a baseline model, and that transfer learning can be useful with a small amount of data as we know it. A [CLS] token containing sentence information as the initial state of the BiLSTM can contribute to a performance improvement in the model.

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An Evolutionary Fake News Detection Method for COVID-19 Pandemic Information

Cited by 81 publications

References 45 publications

A survey of uncover misleading and cyberbullying on social media for public health

A survey of uncover misleading and cyberbullying on social media for public health

Statistical Analysis of Current Financial Instrument Quotes in the Conditions of Market Chaos

Fake Sentence Detection Based on Transfer Learning: Applying to Korean COVID-19 Fake News

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