Why Do Urban Legends Go Viral?
Table 6: Classification Results
['Features', 'UL vs. GN Prec', 'UL vs. GN Rec', 'UL vs. GN F1', 'UL vs. FT Prec', 'UL vs. FT Rec', 'UL vs. FT F1', 'GN vs. FT Prec', 'GN vs. FT Rec', 'GN vs. FT F1']
The goal of our experiments is to understand to what extent it is possible to assign a text to one of the aforementioned classes using just the prototypical characteristics (features) discussed above, and whether there is a subset of features that stands out among the others in this classification task. For every feature combination we conducted a binary classification experiment with ten-fold cross validation on the dataset. We always randomly downsampled the majority class in order to make the dataset balanced, i.e. 50% of positive examples and 50% of negative examples; this accounts for a random baseline of 0.5. We also normalized all features according to z-score.