strategy of always predicting the mode, ‘doesn’t care’. As noted in the experiments section, training samples are weighted inversely to their class representation; therefore, ignoring any class will result in an equal loss. This does not provide the highest classification accuracy. However, we believe when some classes are sparsely populated an MSE optimal classifier that is highly biased toward the mode should not be the standard. For reference, equal sample weights and the same training scheme yield classification accuracy of 36.3% and a weighted f1 score of 31.6%. Five classes—IPA, hates politics, independent, libertarian, and very liberal—have no representation in the test set predictions. The weighted classifier predicts each class at least once.

According to Preotiuc-Pietro et al., all previous research on predicting political ideology from social media text has used binary labels such as liberal vs conservative or Democrat vs Republican. They broaden the classification task to include seven gradations on the liberal to conservative spectrum [@preoctiuc2017beyond]. When predicting ideological tilt from tweets, they achieve a 2.6% boost over baseline (19.6%) with BoW follow by logistic regression. Word2Vec feature embeddings [@mikolov2013distributed] and multi-target learning with some hand-crafted labels yield an 8.0% boost. From classification along grades of a single 