Identifying Characteristic Features of Fake News Articles for Deep Learning-Based Identification
DOI:
https://doi.org/10.58445/rars.314Abstract
Fake news articles rapidly spread online, spreading misinformation, and weakening democracy and credible journalism. This research identifies characteristic stylistic features in the text of fake news articles to build a deep learning-based classifier of news articles, including topics, sentiment, and length of the titles and bodies of articles. An experimental approach is taken to compare the effectiveness of multiple feature selections as input data for the binary classification of fake news articles by a neural network. The top-performing feature selection and neural network architecture result in a classifier that achieves 89.7% accuracy on a testing set.
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