Detection of Online Fake News Using Blending Ensemble Learning

Abstract

The exponential growth in fake news and its inherent threat to democracy, public trust, and justice has escalated the necessity for fake news detection and mitigation. Detecting fake news is a complex challenge as it is intentionally written to mislead and hoodwink. Humans are not good at identifying fake news. The detection of fake news by humans is reported to be at a rate of 54% and an additional 4% is reported in the literature as being speculative. The significance of fighting fake news is exemplified during the present pandemic. Consequently, social networks are ramping up the usage of detection tools and educating the public in recognising fake news. In the literature, it was observed that several machine learning algorithms have been applied to the detection of fake news with limited and mixed success. However, several advanced machine learning models are not being applied, although recent studies are demonstrating the efficacy of the ensemble machine learning approach; hence, the purpose of this study is to assist in the automated detection of fake news. An ensemble approach is adopted to help resolve the identified gap. This study proposed a blended machine learning ensemble model developed from logistic regression, support vector machine, linear discriminant analysis, stochastic gradient descent, and ridge regression, which is then used on a publicly available dataset to predict if a news report is true or not. The proposed model will be appraised with the popular classical machine learning models, while performance metrics such as AUC, ROC, recall, accuracy, precision, and f1-score will be used to measure the performance of the proposed model. Results presented showed that the proposed model outperformed other popular classical machine learning models.

A New Multipredictor Ensemble Decision Framework Based on Deep Reinforcement Learning for Regional GDP Prediction

“…In the research of different GDP impacts, the cross-validation method can accurately analyze the inner connection of the GDP datasets and reduce the forecasting errors [38], [39]. Besides, a single predictive model may only reflect part of the information on the influence factors in GDP analysis [40], [41]. To effectively improve the performance of single predictors and the comprehensive analysis of GDP data, optimization algorithms can be added to the hybrid framework to optimize the input features [42].…”

Machine Learning Methods to Estimate Productivity of Harvesters: Mechanized Timber Harvesting in Brazil

“…Some studies investigated and proposed ways to improve this association, e.g., joint machine learning methods use a meta-model to combine predictive results from heterogeneous base models arranged in at least one layer. Thus, the variation in forecast combinations is based on a data validation set [36][37][38][39].…”

Multimodal Fake-News Recognition Using Ensemble of Deep Learners

“…Existing ensemble fake-news methods [ 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 ] often trained multiple deep or shallow models independently and then combined the outcomes of learners via ensemble mechanisms, such as voting. Thus, these models involve many trainable parameters and a costly training process.…”

“…Then, they selected the three best models using cross-validation and combined the results using the voting mechanism. Hansrajh et al [ 9 ] trained logistic regression, linear discriminant analysis (LDA) classifier, SVM, stochastic gradient descent, and ridge regression on the LIAR dataset [ 29 ]. Then, it employed “blending,” a variant of the stacking mechanism, to fuse the predictions of base models.…”

“…The outputs of the models were concatenated and formed the embedding representation. The representation was then forwarded to a single fully connected layer with one output neuron [ 9 , 30 ].…”