A Few Topical Tweets are Enough for Effective Stance Detection
Table 4: BERT error types with examples
['Error Type', 'No.', 'Examples']
We analyzed all the errors in Set B that were produced by BERT with no expansion, as it represents the best results when expansion is not possible, and those produced by unsupervised classification with the expansion of the test set only, as this produced the best overall results. Since we used BERT to perform tweet-level classification, we manually inspected all 129 misclassified tweets across all topics. Generally we found four types of errors, namely: unexplainable errors where the tweets clearly expressed stance, but the classifier mislabeled them; vague tweets that have no clear clues; tweets in which the user uses the language of the opposing side; and sarcastic tweets.