Credibility-based Fake News Detection
Table 13: Average F1 score using 13 features on separated datasets
['[BOLD] Classifier', '[BOLD]   [ITALIC] F1-macro (Politifact) ', '[BOLD]   [ITALIC] F1-macro (Buzzfeed) ']
This observation shows that content-credibility has very little to add to the performance of fake news prediction in the data. Similarly, for Buzzfeed news data, the best classifier was able to obtain an average F1-macro score of 0.76 with only source-credibility features, whereas with content-credibility features it obtained 0.66. Thus, adding content-credibility features only slightly improved the performance. Our content-credibility features are comparatively fewer than earlier studies, so we emphasize our findings with source-credibility features, which we did not find in earlier research.