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.

Input: Consider Input: This way, we can label 77 new pro-Russian edges by looking at 415 tweets, which means that 19% of the candidates are hits. For the pro-Ukrainian class, we can label 110 new edges by looking at 611 tweets (18% hits). Hence even though the quality of the classifier predictions is too low to be integrated into the network analysis right away, the classifier drastically facilitates the annotation process for human annotators compared to annotating unfiltered tweets (from the original labels we infer that for unfiltered tweets, only 6% are hits for the pro-Russian class, and 11% for the pro-Ukrainian class).

Output: How can the classifier facilitate the annotation task for human annotators?


Input: Consider Input:  In terms of this, rather than starting from Twitter API Search, we look into the archived snapshots of two major news websites (CNN, NBC), and then extract the tweet blocks that are embedded in the news articles.

Output: How do they determine if tweets have been used by journalists?


Input: Consider Input: We extract unique users and tweets from the combined result set to generate a dataset of about 60,000 unique tweets, pertaining to 51,104 unique users.


Output: How many followers did they analyze?