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.

Example Input: News Articles
Our dataset source of news articles is described in BIBREF2. This dataset was built from two different sources, for the trusted news (real news) they sampled news articles from the English Gigaword corpus. For the false news, they collected articles from seven different unreliable news sites. Twitter
For this dataset, we rely on a list of several Twitter accounts for each type of false information from BIBREF6. This list was created based on public resources that annotated suspicious Twitter accounts. The authors in BIBREF6 have built a dataset by collecting tweets from these accounts and they made it available.
Question: What datasets did they use?
Example Output: News Articles Twitter

Example Input: Posts in FBFans dataset are used for this analysis. We calculate the like statistics of each distinct author from these 32,595 posts.
Question: What is the size of the Chinese data?
Example Output: 32,595 posts

Example Input: We first use state-of-the-art PDTB taggers for our baseline BIBREF13 , BIBREF12 for the evaluation of the causality prediction of our models ( BIBREF12 requires sentences extracted from the text as its input, so we used our parser to extract sentences from the message).
Question: What baselines did they consider?
Example Output:
state-of-the-art PDTB taggers