Fake Detect: A Deep Learning Ensemble Model for Fake News Detection

Aslam, Nida; Khan, Irfan Ullah; Alotaibi, Farah Salem; Aldaej, Lama Abdulaziz; Aldubaikil, Asma Khaled

doi:10.1155/2021/5557784

Cited by 80 publications

(40 citation statements)

References 9 publications

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“…Less stiff models are particularly suitable when the transit of fake news is slower and its survival time in the exposed population is higher. The employed model is the standard SIR system of differential Equation (1), but certainly more complex deterministic and stochastic models may be used in order to describe the diffusion of fake news as an epidemic phenomenon as, for instance, in [50][51][52][53][54][55][56][57][58]. Moreover, it would be worth investigating how to detect fake news through sentiment analysis of tweets as suggested by [59,60].…”

Section: Discussionmentioning

confidence: 99%

Stiffness Analysis to Predict the Spread Out of Fake Information

et al. 2021

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This work highlights how the stiffness index, which is often used as a measure of stiffness for differential problems, can be employed to model the spread of fake news. In particular, we show that the higher the stiffness index is, the more rapid the transit of fake news in a given population. The illustration of our idea is presented through the stiffness analysis of the classical SIR model, commonly used to model the spread of epidemics in a given population. Numerical experiments, performed on real data, support the effectiveness of the approach.

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Section: Discussionmentioning

confidence: 99%

Stiffness Analysis to Predict the Spread Out of Fake Information

et al. 2021

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“…They also obtained that TGNF performs better than its variants without the GCL module, and the TDN+TGNF module performs better than GCL+TDN on three datasets. In the paper [11], the researchers presented a Bi-LSTM-GRU-dense deep learning model based on a set of classifiers to classify news as fake or real using LIAR dataset. After experimentations, the results showed that the proposed model achieved an accuracy of 0.898, a recall of 0.916, a precision of 0.913 and an F-score of 0.914, respectively.…”

Section: Related Workmentioning

confidence: 99%

COVID-19 Fake News Detection Using Machine Learning Techniques: A Comparative Study

Lalmi¹,

Djaballah²,

Gharzouli³

2018

EAI Endorsed Transactions on Cloud Systems

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Fake news has become one of the most serious issues in recent years, especially on social media. For example, during the covid-19 pandemic, a great deal of false information about the virus spread easily and quickly through the internet. In this area, researchers have given substantial answers to this problem utilizing various machine learning techniques. However, there are some gaps that need to be clarified. In the context of COVID-19 fake news detection, in this study, we present a comparison of four major machine learning algorithms: SVM, Nave Bayes, Logistic Regression, and Random Forest. We proposed four new machine learning models by combining these algorithms with two feature extraction techniques (TF-IDF and CountVectorizer). On three datasets, we tested the suggested models and analyzed their performance. According to the obtained results, we concluded that some properties of the used datasets can affect the obtained results. In addition, we find the best model overall.

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“…In another work, Aslam et al used LIAR dataset to classify news as real or fake by using an ensemble-based deep learning model. They achieved 91.4% F1-Score from their experiments (Aslam et al, 2021). LekshmiAmmal and Madasamy obtained 88.13% F1-Score by using RoBERTa to predict fake news (Lekshmiammal & Madasamy, 2021).…”

Section: Introductionmentioning

confidence: 99%

AdaBoost Ensemble Learning on top of Naive Bayes Algorithm to Discriminate Fake and Genuine News from Social Media

Bozuyla¹

2021

European Journal of Science and Technology

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There is a continuous increase in social media usage and a huge interaction takes place between users. In this context, fake news circulation or flood becomes a real thread for social media users from various perspectives. Fake news is defined as presentation of misleading information as true news. In this view, fake news is fabricated news that aims to manipulate public opinion to obtain a benefit. For example, increasing readership for profiting through clickbaits is such an aim. Social media users are manipulated through attention grabbing headlines or web-links to increase number of visitors. Therefore, an automated fake news identification model can be used by social media users to filter inadvertent web-traffic. For this goal machine learning algorithms are used in the literature as a solution for fake news problem. In machine learning literature, advancing performance of the base models is crucial. Ensemble learning is one of the key solutions to enhance model efficiency. In this work, we first generated a set of baseline machine learning algorithms and we tested them in terms of their fake news identification ability. We then made use of ensemble learning strategy to further enhance obtained results. More precisely, we obtained Naïve Bayes Multinomial classifier as the best fake news predictor having 96.74 % accuracy. We then further improved this prediction ability to 98.2 % by applying an AdaBoost ensemble learning strategy.

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Fake Detect: A Deep Learning Ensemble Model for Fake News Detection

Cited by 80 publications

References 9 publications

Stiffness Analysis to Predict the Spread Out of Fake Information

Stiffness Analysis to Predict the Spread Out of Fake Information

COVID-19 Fake News Detection Using Machine Learning Techniques: A Comparative Study

AdaBoost Ensemble Learning on top of Naive Bayes Algorithm to Discriminate Fake and Genuine News from Social Media

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