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: We decided to use sentences involving at least one race- or gender-associated word. The sentences were intended to be short and grammatically simple. We also wanted some sentences to include expressions of sentiment and emotion, since the goal is to test sentiment and emotion systems. 
 Question: What criteria are used to select the 8,640 English sentences?

Output: Sentences involving at least one race- or gender-associated word,  sentence  have to be short and grammatically simple,  sentence have to  include expressions of sentiment and emotion.


Input: Consider Input: 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?

Output: chunk consists of a sorted sequence of tweets labeled by the label of its corresponding account


Input: Consider Input: We use the publicly available dataset KVRET BIBREF5 in our experiments. There are 2,425 dialogues for training, 302 for validation and 302 for testing, as shown in the upper half of Table TABREF12. 
 Question: What is the size of the dataset?


Output: 3029