Something’s Brewing!Early Prediction of Controversy-causing Posts from Discussion Features
Table 2: Average accuracy for each post-time, text-only predictor for each dataset, averaged over 15 cross-validation splits; standard errors are ±.6, on average (and never exceed ±1.03). Bold is best in column; underlined are statistically indistinguishable from best in column (p
['[EMPTY]', 'AM', 'AW', 'FT', 'LT', 'PF', 'RL']
Results: In general, the best performing models are based on the BERT features, though HAND+W2V performs well, too. However, no performance gain is achieved when adding hand designed features to BERT. This may be because BERT’s subtokenization scheme incorporates punctuation, link urls, etc., which are similar to the features captured by HAND. Adding an LSTM over BERT features is comparable to mean pooling over the sequence; similarly, considering 128 tokens vs. 512 tokens results in comparable performance. Based on the results of this experiment, we adopt BERT-MP-512 to represent text in experiments for the rest of this work.