EFND: A Semantic, Visual, and Socially Augmented Deep Framework for Extreme Fake News Detection

Nadeem, Muhammad Imran; Ahmed, Kanwal; Li, Dun; Zheng, Zedong; Alkahtani, Hend Khalid; Mostafa, Samih M.; Mamyrbayev, Оrken; Hameed, Hala Abdel

doi:10.3390/su15010133

Cited by 20 publications

(7 citation statements)

References 103 publications

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“…The study reveals that the use of transfer learning from XLM-RoBERTa, mBERT and ELECTRA was able to achieve increased efficiency of fake news detection in Hindi [14]. The study of [15] presents a technique for detecting fake news using multimodal EFND. A Multilayer Perceptron was implemented and the results of the study showed that for the PolitiFact and GossipCop datasets, the EFND achieved an accuracy of 0.988% and 0.990%, respectively [15].…”

Section: Introductionmentioning

confidence: 95%

Comparative Analysis of Machine Learning Models for Detection of Fake News: A Case Study of Covid-19

Olayiwola,

Oyedeji,

Omoyeni

et al. 2023

j.inf.tech.comp.eng

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During and after the Covid-19 pandemic, people rely heavily on the internet for information because of its easy accessibility. However, the spread of fake information through this medium has been fast-growing, especially during and after the pandemic. This study, therefore, aims to evaluate the performance of 5 machine learning models used in detecting Covid-19 fake news. The models were trained using the Covid-19 dataset gathered online. The dataset contains 7,262 real news and 9,727 fake news, totalling 16,989 news altogether. 80% of this dataset was used for training the models while 20% was used for testing them. The support vector machine (SVM) with 95%, 95%, 97% and 96% for the accuracy, precision, recall and F1-score respectively was the best classifier for detecting Covid-19 fake news and has shown a better performance than the other algorithms.

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Section: Introductionmentioning

confidence: 95%

Comparative Analysis of Machine Learning Models for Detection of Fake News: A Case Study of Covid-19

Olayiwola,

Oyedeji,

Omoyeni

et al. 2023

j.inf.tech.comp.eng

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show abstract

“…Finally, a fused Multilayer Perceptron model is utilized to classifier fake news. PolitiFact and GossipCop dataset provide 98.8% and 99% accuracy respectively [18]. Elhadad et al suggested textual metadata for analysis across three openly accessible datasets (ISOT, FA-KES, and LIAR) and combinations of features derived from online news through experimentation.…”

Section: Literature Reviewsmentioning

confidence: 99%

Web-Based Machine Learning Framework for Authenticating News: A Fake News Detection System

Rahman,

Sumon

et al. 2024

Preprint

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Today fake news is curse for the society. Fake news has a bad impact on every human life. Disinformation is being spread more and more via fake news, which manipulates people's perceptions and knowledge to affect their awareness and judgment. Social media are mainly responsible for propagating fake news. Misinformation disseminated through digital platforms creates not only confusion but also cultivates biased perspectives among individuals. To prevent the problematic effects of falsified information we have to identify it first more quickly. This paper suggests the implementation of a supervised machine learning system designed to identify fake news through website analysis in this research, TF-IDF Vectorizer model is utilized for features extraction and thirteen models such as Random Forest (RF), Decision Tree (DT), Bernoulli Naïve Bayes (BNB), Multinomial Naïve Bayes (MNB), Support Vector Machine (SVM), Linear Support Vector Classifier (LSVC), K-Nearest Neighbor (KNN), XGBoost (XB), Multilayer Perceptron (MLP), Extra Trees (ET), AdaBoost (AB), Gradient Boosting (GB) classifier are used to classifier fake news. The proposed approach used about 20,800 groups of data to test the suggested framework on a significant number of articles published through Kaggle. The recommended Linear Support Vector Classifier model outperforms the other twelve techniques, according to numerous approaches on this dataset. LSVC algorithm gives the highest 99.38% accuracy to classifier the fake news from monolingual text dataset.

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“…A new area of research receiving interest internationally is that of Arvinder Bali told all colleagues investigated that Gradient Boostingfared better than other classifiers, with an calculated F1-Score of 0.91 and an accuracy of 88%, so for the purpose of studying the intricate media landscape [8].The characteristics and patterns of false newshave been uncovered through research, and certain models have proved successful in telling the difference between the two. [9] These models, which are based on certain traits designed for spotting particular kinds of fake news, let us assess digital information and draw defensible judgments.…”

Section: A Detection According To News Contentmentioning

confidence: 99%

Unmasking Disinformation: Advanced Techniques for Fake News Detection and Mitigation

Srivastava,

Nitasha,

Akansha

et al. 2023

IJRASET

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With the current increase in social media usage, everyone is very concerned about the spread of misleading information. Misinformation has been employed to sway public opinion, impact the 2016 US Presidential Election, and disseminate animosity and turmoil, such the genocide against the Rohingya people. A 2018 MIT study found that on Twitter, bogus news spreads six times faster than real news. In addition, there is now a problem in the news media's reliability and credibility. It is getting harder and harder to tell the difference between the morphed and true news, and for the evaluation of this study, a combination of various machine learning techniques, methods along with the natural language processing (NLP), LSTM, and passive aggressive classifier (PAC), to distinguish between bogus and authentic news and these two have shown to be the most successful machine learning models, despite the availability of many others.

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EFND: A Semantic, Visual, and Socially Augmented Deep Framework for Extreme Fake News Detection

Cited by 20 publications

References 103 publications

Comparative Analysis of Machine Learning Models for Detection of Fake News: A Case Study of Covid-19

Comparative Analysis of Machine Learning Models for Detection of Fake News: A Case Study of Covid-19

Web-Based Machine Learning Framework for Authenticating News: A Fake News Detection System

Unmasking Disinformation: Advanced Techniques for Fake News Detection and Mitigation

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