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]: 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 summaries generated by our ILP-based approach were compared with the corresponding manual summaries using the ROUGE BIBREF22 unigram score.
Question: What evaluation metrics were used for the summarization task?
[EX A]: ROUGE BIBREF22 unigram score

[EX Q]: Meanwhile, attending colleges in the Northeast, West and South regions increases the possibility of posting about sexual harassment (positive coefficients), over the Midwest region.
Question: Which geographical regions correlate to the trend?
[EX A]:
Northeast U.S., West U.S. and South U.S.