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Media news are making a large part of public opinion and, therefore, must notbe fake. News on web sites, blogs, and social media must be analyzed beforebeing published. In this paper, we present linguistic characteristics of medianews items to differentiate between fake news and real news using machinelearning algorithms. Neural fake news generation, headlines created bymachines, semantic incongruities in text and image captions generated bymachine are other types of fake news problems. These problems use neuralnetworks which mainly control distributional features rather than evidence. Wepropose applying correlation between features set and class, and correlationamong the features to compute correlation attribute evaluation metric andcovariance metric to compute variance of attributes over the news items.Features unique, negative, positive, and cardinal numbers with high values onthe metrics are observed to provide a high area under the curve (AUC) andF1-score.

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