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.

[EX Q]: 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?
[EX A]: News Articles Twitter

[EX Q]: Consequently, we investigate ways to detect suspicious accounts by considering their tweets in groups (chunks). Our hypothesis is that suspicious accounts have a unique pattern in posting tweet sequences. Since their intention is to mislead, the way they transition from one set of tweets to the next has a hidden signature, biased by their intentions. Given a news Twitter account, we read its tweets from the account's timeline. Then we sort the tweets by the posting date in ascending way and we split them into $N$ chunks. Each chunk consists of a sorted sequence of tweets labeled by the label of its corresponding account.
Question: How is a "chunk of posts" defined in this work?
[EX A]: chunk consists of a sorted sequence of tweets labeled by the label of its corresponding account

[EX Q]: The whole dataset consists of 81,826 samples annotated by native English speakers. 80% of them are used as training set. 10% of them are used as validation set while the rest is used as test set. 3000 hard samples are selected from the test set.
Question: What is the difference between the full test set and the hard test set?
[EX A]:
3000 hard samples are selected from the test set