Abstract
Fake news has grown in popularity and spread as a result of increased insecurity, political events, and pandemics, among other things. This study used an ensemble machine learning technique to better predict fake news on social media based on the content of news articles. The proposed model used a soft voting classifier to aggregate four machine learning algorithms, namely, Naïve Bayes, Support Vector Machine (SVM), and Logistic Regression, for the classification of news articles as fake or real. GridSearchCV was used to fine-tune the algorithms to get the optimal results during the training process. A Kaggle dataset was used for the experiment; it was comprised of both false and true news. Performance evaluation metrics were used to measure the performance of the base learners and our proposed ensemble technique on the dataset. The results of our experiment show that the proposed ensemble approach produced the highest accuracy, precision, recall, and F1_score values of 93%, 94%, 92%, and 93%, respectively, on the dataset as compared to the individual learners. This approach may also be used in other classification techniques for spam detection, sentiment analysis, and prediction of loan eligibility, among other things.
Highlights
Due to the accessibility, affordability, and convenience of use of social media platforms, a large number of individuals utilize social media to get their daily news
Social media contributes to the increase in the popularity and propagation of fake news due to its flexibility, convenience, and affordability
The proposed system creates a better model by aggregating the base learners into a model that predicts by majority vote using a soft voting classifier
Summary
Affordability, and convenience of use of social media platforms, a large number of individuals utilize social media to get their daily news. Fake news is a piece of news that is deliberately and verifiably false and could mislead consumers [1]. Authenticity and intent are two vital characteristics in this definition According to this definition, a news article must be verified, and the intent must be known before it can be flagged as fake. A news article must be verified, and the intent must be known before it can be flagged as fake They chose this definition primarily to eliminate ambiguities between false news and similar notions. This definition was embraced by many researchers, including [2]. Despite the fact that satire is frequently created for entertainment and recognizes its deception to consumers, some papers regard satire news as fake news because the contents are false [3]. Hoaxes created only for the purpose of amusement or to defraud certain individuals
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