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.

Input: Consider Input: Fenerbahçe We have decided to consider tweets about popular sports clubs as our domain for stance detection.  Hence we have determined our targets as Galatasaray (namely Target-1) and Fenerbahçe (namely, Target-2) which are two of the most popular football clubs in Turkey.  Then, we have annotated the stance information in the tweets for these targets as Favor or Against. For the purposes of the current study, we have not annotated any tweets with the Neither class. 
 Question: How were the tweets annotated?

Output: tweets are annotated with only Favor or Against for two targets - Galatasaray and Fenerbahçe


Input: Consider 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?

Output: News Articles Twitter


Input: Consider Input: BIBREF5 adopt a machine learning approach for NER. Their NER system extracts medical problems, tests and treatments from discharge summaries and progress notes. The dataset used is the i2b2 2010 challenge dataset.  
 Question: Does the paper explore extraction from electronic health records?


Output: Yes