A Semi-supervised Learning Method for Fake News Detection in Social Media

Mansouri, Reza; Naderan-Tahan, Mahmood; Rashti, Mohammad Javad

doi:10.1109/icee50131.2020.9261053

Cited by 21 publications

(13 citation statements)

References 11 publications

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“…For this purpose, twenty three (23) features were extracted from the text of the dataset. Four feature selection techniques like chi-square, Univariate, features importance and information gain, were used to select fourteen (14) best features out of the twenty three (23) extracted features. Proposed model as well as other models were used for Figures…”

Section: Discussionmentioning

confidence: 99%

“…Random forest and XGB performed best using handcraft features, web-based networking media. In study [14] using Naïve Bayes classi er, SVM with comparison Naïve Bayes and CNN. Results show that Naïve Bayes, SVM, NLP are performed better than other machine classi er.…”

Section: Related Workmentioning

confidence: 99%

“…classi cation of fake news and real news using fourteen (14) best features. The experimental results show that proposed model outperformed all other classi ers in term of better classi cation accuracy in fake news prediction.…”

Section: Declarationsmentioning

confidence: 99%

“…So it is important to strip out non-valuable and less important features to reduce model complexity and increase model accuracy. Therefore, four different feature selection techniques are used, such as Chi-Square, Feature Importance, Information Gain and Univariate, to pick the fourteen (14) best features out of 23 features.…”

Section: Introductionmentioning

confidence: 99%

“…In fourth step, fourteen(14) best features are selected by using the feature importance technique. All models alongwith proposed model are evaluated on top 14 best features and results are stored in…”

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Machine learning for fake news classification with optimal feature selection

et al. 2022

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Nowadays, information is published in newspapers and social media while transmitted on radio and television about current events and speci c elds of interest nationwide and abroad. It becomes di cult to explicit what is real and what is fake due to the explosive growth of online content. As a result, fake news has become epidemic and immensely challenging to analyze fake news to be veri ed by the producers in the form of data process outlets not to mislead the people. Indeed, it is a big challenge to the government and public to debate the situation depending on case to case. For the purpose several websites were developed for this purpose to classify the news as either real or fake depending on the website logic and algorithm. A mechanism has to be taken on fact-checking rumors and statements, particularly those that get thousands of views and likes before being debunked and refuted by expert sources. Various machine learning techniques have been used to detect and correctly classi ed of fake news. However, these approaches are restricted in terms of accuracy. This study has applied a Random Forest (RF) classi er to predict fake or real news. For this prpose, twenty-three (23) textual features are extracted from ISOT Fake News Dataset. Four best feature selection techniques like Chi2, Univariate, information gain and Feature importance are used for selecting fourteen best features out of twentythree. The proposed model and other benchmark techniques are evaluated on the dataset by using best features. Experimental ndings show that, the proposed model outperformed state-of-the-art machine learning techniques such as GBM, XGBoost and Ada Boost Regression Model in terms of classi cation accuracy.

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Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Declarationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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