instruction:
In this task, you will be presented with a context from an academic paper and you have to write an answerable question based on the context. Your questions can be extractive, abstractive, or yes-no questions.
question:
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. 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. Therefore, reading these tweets in chunks has the potential to improve the detection of the fake news accounts.
answer:
How are chunks defined?


question:
Finally, we performed hyperparameter tuning by adjusting the number of coattention blocks, the batch size, and the number of epochs trained and ensembled our three best networks.
answer:
What hyperparameters have been tuned?


question:
In order to compare with the previous methods, we evaluate our model on AIDA-B and other datasets.  AIDA-CoNLL BIBREF14 is annotated on Reuters news articles. It contains training (AIDA-Train), validation (AIDA-A) and test (AIDA-B) sets.

ACE2004 BIBREF15 is a subset of the ACE2004 Coreference documents.

MSNBC BIBREF16 contains top two stories in the ten news categories(Politics, Business, Sports etc.)

AQUAINT BIBREF17 is a news corpus from the Xinhua News Service, the New York Times, and the Associated Press.

WNED-CWEB BIBREF18 is randomly picked from the FACC1 annotated ClueWeb 2012 dataset.

WNED-WIKI BIBREF18 is crawled from Wikipedia pages with its original hyperlink annotation.
answer:

What datasets used for evaluation?