Predicting the Type and Target of Offensive Posts in Social Media
Table 3: Distribution of label combinations in OLID.
['[BOLD] A', '[BOLD] B', '[BOLD] C', '[BOLD] Training', '[BOLD] Test', '[BOLD] Total']
It is worth noting that one of the key challenges we observed when collecting for OLID was producing a dataset containing a sufficient number of instances for each class. This is particularly evident in the sizes for Subtasks B and C. Other studies also had this issue when collecting similar datasets. For example, in Davidson et al. only 11.6% of the examples were labeled as hate speech. Our models are trained on the training dataset, and evaluated by predicting the labels for the held-out test set. We further report per-class Precision (P), Recall (R), and F1-score (F1), and weighted average. Finally, we compare the performance of the models against simple majority and minority class baselines.