A Stylometric Inquiry into Hyperpartisan and Fake News
Table 5: Accuracies, class-wise precision, recall and F-Measure of predicting fake news by features and classifier.
['[BOLD] Features', '[BOLD] Accuracy all', '[BOLD] Precision fake', '[BOLD] Precision real', '[BOLD] Recall fake', '[BOLD] Recall real', '[BOLD] F [BOLD] 1 fake', '[BOLD] F [BOLD] 1 real']
In both cases, values are again averages of 3-fold cross-validation. Although all classifiers outperform the naive baselines of classifying everything as one class in terms of precision, the slight increase comes at the cost of a large decrease in recall. While the orientation-specific classifiers are slightly better for most metrics, none of them can outperform the naive baselines with regard to the F-Measure.