Machine learning for fake news classification with optimal feature selection

Abstract

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.

ANN: adversarial news net for robust fake news classification

“…These methods are constrained in their accuracy, though. Reference 9 used a Random Forest (RF) classifier to ascertain whether a news article is fake. To accomplish this, 23 textual characteristics are selected from the dataset, and four feature selection methods are utilized to pick the best 14 features out of 23.…”

“…Additional factors such as the news title, source, and engagement statistics could improve classification accuracy. Therefore, the use of multiple attributes should be explored to enhance classification accuracy 8 2020 ML methods (Logistic regression, SVM, multilayer perceptron, KNN), ensemble learners (random forest, bagging ensemble classifier, boosting ensemble, Voting ensemble classifier) Classify news articles by identifying patterns in textual data The dataset was a collection of news articles from multiple domains retrieved from the World Wide Web Accuracy, precision, recall, and F1-score Compared to the individual learners, the ensemble learners have demonstrated superior performance across all performance metrics Key sources that are involved in the spread of fake news need to be identified 9 2022 Random forest classifier To ascertain whether a news article is fake ISOT dataset Accuracy The suggested framework surpasses state-of-the-art ML algorithms by an accuracy of 96.42% This study is only restricted to ML methods 10 2023 Individual ML methods (KNN, decision tree), In-built ensembled methods (random forest, gradient boosting), and custom ensemble classifiers (stacking, maximum voting algorithms) To categorize a specific news item as real or fraudulent Datasets of fake and true news are utilized Accuracy Combining three separate ML models, namely KNN, SVM, and Logistic Regression, into a custom ensembled model, this paper has achieved a classification accuracy of 91.5% in distinguishing between true and fake news The scope of this study is limited exclusively to ML methods 11 2021 BERT-based deep convolutional approach To detect fake news in social media Real-world fake news dataset Accuracy, cross entropy loss, false positive rate, and false negative rate The suggested model achieved an accuracy of 98.90% It did not utilize a hybrid approach incorporating content, context, and temporal-level information from news articles for binary and multi-class real-world fake news datasets 12 …”

Explaining Xgboost Predictions With Shap Value: A Comprehensive Guide to Interpreting Decision Tree-Based Models

“…Univariate Feature Selection: This method involves evaluating each feature independently using statistical tests or other criteria, and selecting the most important features based on their individual performance (Jain & Saha, 2022;Fagrou et al, 2022;Fayaz et al, 2022).…”

Fake News Detection System: An implementation of BERT and Boosting Algorithm

“…Different findings are shown by other classification models, such as Passive Aggressive Classifiers, Logistic Regression Classifiers, and Random Forest Classifiers. Another study examines the rapid expansion of online news content and establishes whether the news is true or false [20]. For this reason, the research suggests a mechanism to identify rumors and claims that need to be fact-checked, particularly those that receive thousands of views and likes before being refuted and debunked by reliable sources.…”