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.

We used dataset, HEOT obtained from one of the past studies done by Mathur et al. where they annotated a set of cleaned tweets obtained from twitter for the conversations happening in Indian subcontinent. A labelled dataset for a corresponding english tweets were also obtained from a study conducted by Davidson et al. 
 Question: How is the dataset collected?
A labelled dataset for a corresponding english tweets were also obtained from a study conducted by Davidson et al HEOT obtained from one of the past studies done by Mathur et al

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?
words embeddings, style, and morality features

We started by answering always YES (in batch 2 and 3) to get the baseline performance. For batch 4 we used entailment. 
 Question: What was the baseline model?

by answering always YES (in batch 2 and 3)