Feature Selection for Fake News Classification

Abstract

An explosive growth of misleading and untrustworthy news articles has been observed over the last years. These news articles are often referred to as fake news and have been found to severely impact fair elections and democratic values. Computational Intelligence models may be applied to the classification of news articles, assuming that an efficient feature set is available as input to the model. However, the selection of appropriate feature sets is an open question for such high-dimensional tasks. A further challenge is the general applicability of feature selection strategies, where testing on a single dataset may convey misleading results. The work herein evaluates a wide-range of potential news article features resulting in twenty-five potential features. Feature selection, based on a combination of feature scoring, feature ranking and mutual information is then applied, evaluated on multiple datasets: Kaggle, Liar and FakeNewsNet. An Artificial Immune System model is applied in the feature ranking and as the classification model. The accuracy obtained is compared to state of the art fake news classification models, highlighting that the approach shows promise in terms of accuracy despite the small feature sets provided for classification.