Predictive linguistic cues for fake news: a societal artificial intelligence problem

TL;DR

This paper explores linguistic features of news articles to distinguish fake news from real news using machine learning, focusing on correlation metrics and feature analysis to improve detection accuracy.

Contribution

It introduces a novel approach applying correlation and covariance metrics to linguistic features for fake news detection, emphasizing features like unique words and sentiment.

Findings

High AUC and F1-score achieved with selected features

Correlation metrics effectively differentiate fake from real news

Linguistic cues are significant indicators for fake news detection

Abstract

Media news are making a large part of public opinion and, therefore, must not be fake. News on web sites, blogs, and social media must be analyzed before being published. In this paper, we present linguistic characteristics of media news items to differentiate between fake news and real news using machine learning algorithms. Neural fake news generation, headlines created by machines, semantic incongruities in text and image captions generated by machine are other types of fake news problems. These problems use neural networks which mainly control distributional features rather than evidence. We propose applying correlation between features set and class, and correlation among the features to compute correlation attribute evaluation metric and covariance metric to compute variance of attributes over the news items. Features unique, negative, positive, and cardinal numbers with high values on the metrics are observed to provide a high area under the curve (AUC) and F1-score.