Quantitative results are given only for the author's PSL model and not compared against any traditional baseline classification algorithms, making it unclear to what degree their model is necessary. Poor comparison with alternative approaches makes it difficult to know what to take away from the paper.
The qualitative investigation is interesting, but the chosen visualizations are difficult to make sense of and add little to the discussion. Perhaps it would make sense to collapse across individual politicians to create a clearer visual.
- General Discussion: The submission is well written and covers a topic which may be of interest to the ACL community. At the same time, it lacks proper quantitative baselines for comparison. Minor comments: - line 82: A year should be provided for the Boydstun et al. citation - It’s unclear to me why similar behavior (time of tweeting) should necessarily be indicative of similar framing and no citation was given to support this assumption in the model.
- The related work goes over quite a number of areas, but glosses over the work most clearly related (e.g. PSL models and political discourse work) while spending too much time mentioning work that is only tangential (e.g. unsupervised models using Twitter data).
- Section 4.2 it is unclear whether Word2Vec was trained on their dataset or if they used pre-trained embeddings.
- The authors give no intuition behind why unigrams are used to predict frames, while bigrams/trigrams are used to predict party.
- The authors note that temporal similarity worked best with one hour chunks, but make no mention of how important this assumption is to their results. If the authors are unable to provide full results for this work, it would still be worthwhile to give the reader a sense of what performance would look like if the time window were widened.
- Table 4: Caption should make it clear these are F1 scores as well as clarifying how the F1 score is weighted (e.g. micro/macro). This should also be made clear in the “evaluation metrics” section on page 6.
- Table 4: Caption should make it clear these are F1 scores as well as clarifying how the F1 score is weighted (e.g. micro/macro). This should also be made clear in the “evaluation metrics” section on page 6.