Classification Of Fake News Headline Based On Neural Networks

TL;DR

This paper presents a neural network-based approach for classifying news headlines as fake or real, utilizing TF-IDF features and achieving high accuracy on a large Kaggle dataset.

Contribution

It introduces a neural network model for fake news headline classification that outperforms other models in accuracy on a comprehensive dataset.

Findings

Neural network achieved 86.22% accuracy.

TF-IDF effectively extracted features for classification.

Model outperformed other compared models in accuracy.

Abstract

Over the last few years, Text classification is one of the fundamental tasks in natural language processing (NLP) in which the objective is to categorize text documents into one of the predefined classes. The news is full of our life. Therefore, news headlines classification is a crucial task to connect users with the right news. The news headline classification is a kind of text classification, which can be generally divided into three mainly parts: feature extraction, classifier selection, and evaluations. In this article, we use the dataset, containing news over a period of eighteen years provided by Kaggle platform to classify news headlines. We choose TF-IDF to extract features and neural network as the classifier, while the evaluation metrics is accuracy. From the experiment result, it is obvious that our NN model has the best performance among these models in the metrics of accuracy. The higher the accuracy is, the better performance the model will gain. Our NN model owns the accuracy 0.8622, which is highest accuracy among these four models. And it is 0.0134, 0.033, 0.080 higher than its of other models.