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.

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?

A: chunk consists of a sorted sequence of tweets labeled by the label of its corresponding account
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Q: The ensembles were formed by simply averaging the predictions from the constituent single models. 
 Question: How does their ensemble method work?

A: simply averaging the predictions from the constituent single models
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Q: We empirically test our approach on a series of experiments on WikiTableQuestions, to our knowledge the only dataset designed for this task. In Algorithm 1 we describe how logical forms are transformed into interpretable textual representations called "paraphrases". 
 Question: What is the source of the paraphrases of the questions?

A:
WikiTableQuestions
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