Feature Extraction of Text for Deep Learning Algorithms: Application on Fake News Detection

TL;DR

This paper demonstrates that alphabet frequency features, when used with deep learning, can effectively classify fake news with high accuracy, simplifying feature extraction in natural language processing.

Contribution

It introduces a novel approach of using alphabet frequencies alone for fake news detection, bypassing complex sequence-based features.

Findings

Achieved 85% accuracy in fake news classification

Alphabet frequencies contain useful features for text understanding

Simplifies feature extraction process for NLP tasks

Abstract

Feature extraction is an important process of machine learning and deep learning, as the process make algorithms function more efficiently, and also accurate. In natural language processing used in deception detection such as fake news detection, several ways of feature extraction in statistical aspect had been introduced (e.g. N-gram). In this research, it will be shown that by using deep learning algorithms and alphabet frequencies of the original text of a news without any information about the sequence of the alphabet can actually be used to classify fake news and trustworthy ones in high accuracy (85\%). As this pre-processing method makes the data notably compact but also include the feature that is needed for the classifier, it seems that alphabet frequencies contains some useful features for understanding complex context or meaning of the original text.