\csq@thequote@oinit\csq@thequote@oopenDid I Say Something Wrong?\csq@thequote@ocloseA Word-Level Analysis of Wikipedia Articles for Deletion Discussions
Table 4: This table shows the performance of the SVM, NB and LM classifiers using the sliding window approach with linear sampling. RapidMiner did not return AUC values for the SVM and NB classifier, so they had to be left out. Function words classifications are marked with “FW”. All others were full text classifications. The plus and minus symbols indicate whether the performance metric was calculated for disruptive (+) or constructive contributions (−).
['[BOLD] Classifier', '[BOLD] Recall+', '[BOLD] Recall−', '[BOLD] Precision+', '[BOLD] Precision−', '[BOLD] F1+', '[BOLD] F1−', '[BOLD] Accuracy', '[BOLD] AUC']
The number of constructive and disruptive posts was increased compared to the independent posts classifications. This is conditioned by the sliding window algorithm which regards all merged posts containing a blocked post as disruptive. As this approach considers an editor’s post history, the data is different and the approach was evaluated on newly sampled data. However, multiple runs of classifications using both the independent posts as well as the sliding window approach showed that the results always remained similar.