Abstract
With the increasing popularity of social media, people has changed the way they access news. News online has become the major source of information for people. However, much information appearing on the Internet is dubious and even intended to mislead. Some fake news are so similar to the real ones that it is difficult for human to identify them. Therefore, automated fake news detection tools like machine learning and deep learning models have become an essential requirement. In this paper, we evaluated the performance of five machine learning models and three deep learning models on two fake and real news datasets of different size with hold out cross validation. We also used term frequency, term frequency-inverse document frequency and embedding techniques to obtain text representation for machine learning and deep learning models respectively. To evaluate models' performance, we used accuracy, precision, recall and F1-score as the evaluation metrics and a corrected version of McNemar's test to determine if models' performance is significantly different. Then, we proposed our novel stacking model which achieved testing accuracy of 99.94% and 96.05 % respectively on the ISOT dataset and KDnugget dataset. Furthermore, the performance of our proposed method is high as compared to baseline methods. Thus, we highly recommend it for fake news detection.
Highlights
With the rapid development of the Internet, social media has become an perfect hotbed for spreading fake news, distorted information, fake reviews, rumors, satires
We evaluated different classification algorithms such as logistic regression (LR) [44], supports vector machine (SVM) [10], k-nearest neighbor (k-NN) [44], decision tree (DT) [41], random forest (RF), convolutional neural network (CNN), gated recurrent network (GRU) [9], long
The experimentation on both ISOT and KDnugget datasets was performed by using Google Colab, a free cloud service supported by Google
Summary
With the rapid development of the Internet, social media has become an perfect hotbed for spreading fake news, distorted information, fake reviews, rumors, satires. Many people think the 2016 U.S presidential election campaign has been influenced by fake news. Subsequent to this election, the term has entered the mainstream vernacular [45]. Nowadays fake news has become a major concern for both industry and academia, one of the solutions for this problem is human fact-checking. The real-time nature of fake news on social media makes identify online fake news even more difficult [45]. The expert fact-checking may have very limited help because of its low efficiency. Fact-checking by human is very laborious and expensive
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