Predictive Linguistic Cues for Fake News: A Societal AI Problem
Published in IAES International Journal of Artificial Intelligence (IJ-AI), 2022
Recommended citation: Sandhya Aneja, Nagender Aneja, Ponnurangam Kumaraguru "Predictive Linguistic Cues for Fake News: A Societal AI Problem." IAES International Journal of Artificial Intelligence (IJ-AI), 2022. vol. 11( 4) doi: 10.11591/ijai.v11.i4.pp1252-1260 https://ijai.iaescore.com/index.php/IJAI/article/view/21798
(Journal Publication)
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.