Controversy in Context
Table 1: Accuracy obtained by controversiality classifiers with 10-fold cross validation.
['[BOLD] Classifier', '[BOLD] Dataset', '[BOLD] Acc']
The most accurate one, the Structure classifier, builds, among others, collaboration networks by considering high-level behavior of editors both in their individual forms, and their pairwise interactions. A collaboration profile containing these individual and pairwise features is built for each two interacting editors and is classified based on the agreement or disagreement relation between them. This intensive computation renders that classifier impractical. We also observe the superiority of classifiers that directly access the context (NB and RNN) over classifiers that access it via word embedding (NN). results obtained when models trained on Dataset I are applied to Dataset III. For this experiment we also included a BERT network Devlin et al. fine tuned on Dataset I. The Pearson correlation between the scores obtained via manual annotation and the scores generated by our automatic estimators suggests a rather strong linear relationship between the two. Accuracy was computed as for previous datasets, by taking here as positive examples the concepts receiving 6 or more positive votes, and as negative a random sample of 670 concepts out of the 1182 concepts receiving no positive vote.