Why Do Urban Legends Go Viral?
Table 8: Results for UL vs FT vs GN
['Features', 'F1 [ITALIC] μ', 'F1 [ITALIC] σ']
Three Class Classification. Finally we also tested feature predictivity on a three class classification task (UL vs GN vs FT). Since in this case we did not performed downsampling, we use the ZeroR classifier as a baseline. MCC returns a value between -1 and +1, where +1 represents a perfect prediction, 0 no better than random and -1 indicates total disagreement. Results are consistent with previous experiments. While between UL and GN the discrimination is given by a skillful mixture of all the prototypical features together, where none has a clear predominance over the others, between UL and FT, readability (READ) and affect (AFF) play a major role. we see that while ALL features together have the highest averaged F1, READ is the best performing subset of features in all experiments, followed by AFF, NER and TIMEX that perform reasonably well. n general , these experiments proved the goodness of our features in discriminating UL against FT and GN in a machine learning framework, confirming the results emerged from the quantitative analysis part. In particular, as expected, these features gave the best results in the GN vs FT experiments, showing that these two genres represent the extremes of a continuum where ULs are placed.