In this task, you will be presented with a context from an academic paper and a question separated with a
. You have to answer the question based on the context.
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Question: The results indicate that most performance gains come from words embeddings, style, and morality features. Other features (emotion and sentiment) show lower importance: nevertheless, they still improve the overall system performance (on average 0.35% Macro-F$_1$ improvement)
Question: Based on this paper, what is the more predictive set of features to detect fake news?

Answer: words embeddings, style, and morality features

Question: To evaluate real-world performance of our selected classifier (i.e., performance in the absence of model and parameter bias), we perform classification of the holdout set. On this set, our classifier had an accuracy and F1-score of 89.6% and 89.2%, respectively.
Question: What was their system's performance?

Answer: accuracy and F1-score of 89.6% and 89.2%, respectively

Question: One-Vs-The-Rest strategy was adopted for the task of multi-class classification and I reported F-score, micro-F, macro-F and weighted-F scores using 10-fold cross-validation.
Question: What metrics are considered?

Answer:
F-score micro-F macro-F weighted-F