NetSpam: a Network-based Spam Detection Framework for Reviews in Online Social Media
TABLE V: Weights of all features (using unsupervised approach); features are ranked based on their overall average weights.
['Dataset - Weights', 'DEV', 'NR', 'ETF', 'BST', 'RES', 'PP1', 'ACS', 'MCS']
Iv-C3 Unsupervised Method One of the achievement in this study is that even without using a train set, we can still find the best set of features which yield to the best performance. As it is explained in Sec. As shown in Fig. (p-value=0.0208) and for SPeaglePlus this value reach 0.90 (p=0.0021). As another example for user-based dataset there is a correlation equal to 0.93 (p=0.0006) for NetSpam, while for SPeagle this value is equal to 0.89 (p=0.0024). This observation indicates NetSpam can prioritize features for both frameworks. For all of them, DEV is most weighted features, followed by NR, ETF and BST.