\csq@thequote@oinit\csq@thequote@oopenDid I Say Something Wrong?\csq@thequote@ocloseA Word-Level Analysis of Wikipedia Articles for Deletion Discussions
Table 5: This table shows the performance of the SVM, NB and LM classifiers using the independent posts approach on the oldest discussion. 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']
Although we only present the data of a single run each, all tests have been executed multiple times with newly sampled data sets. This confirmed that the observed performances were not the result of a sampling bias. The results always remained comparable but for one exception. When operating on a data set chronologically sampled from the earliest \glspldiscussion, we found that the classifiers yielded significantly improved performance. Chronologically sampling data from more recent \glspldiscussion could not reproduce these results. However, due to the amount of existing data, we were not able to test all possible partitions.