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


[Q]: Our in-house dataset includes manually annotated RE data for 6 languages: English, German, Spanish, Italian, Japanese and Portuguese.  The ACE05 dataset includes manually annotated RE data for 3 languages: English, Arabic and Chinese. It defines 7 entity types (Person, Organization, Geo-Political Entity, Location, Facility, Weapon, Vehicle) and 6 relation types between the entities (Agent-Artifact, General-Affiliation, ORG-Affiliation, Part-Whole, Personal-Social, Physical). 
 Question: What languages do they experiment on?
[A]: English, German, Spanish, Italian, Japanese and Portuguese  English, Arabic and Chinese


[Q]: To better demonstrate the effectiveness of the proposed model, we compare with baselines and show the results in Table TABREF12 . 
 Question: What was the score of the proposed model?
[A]:
Best results authors obtain is EM 51.10 and F1 63.11