Simple Open Stance Classification for Rumour Analysis
Table 5: Contribution of each AF feature. Accuracy scores are for the Random Forest classifier on RumourEval data set with each feature removed in turn.
['[BOLD] Features', '[BOLD] Accuracy']
The results indicate that each AF feature contributes to the accuracy boost in stance classification. The highest accuracy loss results from removing the surprise (SS) and certainty (NDS) scores and the least – when the isQuestion (IQ) feature is removed. None of the AF feature removals cause a significant drop in accuracy. However, the loss is significant (p<0.0001) when all AF features are removed.