instruction:
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.
question:
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?
answer:
chunk consists of a sorted sequence of tweets labeled by the label of its corresponding account


question:
The test-set accuracies obtained by different learning methods, including the current state-of-the-art results, are presented in Table TABREF11 . 
 Question: what models did they compare to?
answer:
High-order CNN, Tree-LSTM, DRNN, DCNN, CNN-MC, NBoW and SVM 


question:
Although MCDN doesn't obtain the highest precision, it increases F1-score by 10.2% and 3% compared with the existing best systems $LS \cup KLD \cup CONN$ and $KLD \cup LS \cup LS_{inter}$. 
 Question: What performance did proposed method achieve, how much better is than previous state-of-the-art?
answer:

increases F1-score by 10.2% and 3% compared with the existing best systems $LS \cup KLD \cup CONN$ and $KLD \cup LS \cup LS_{inter}$