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.

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

Example Input: To assess the consistency of annotations and also eliminate coincidental annotations, we used agreement rates, which is calculated by dividing the number of senses under each category where the annotators annotate consistently by the total number of each kind of sense. And considering the potential impact of unbalanced distribution of senses, we also used the Kappa value.
Example Output: Which inter-annotator metric do they use?

Example Input: As neural classification model, we use a convolutional neural network (CNN) BIBREF29, which has previously shown good results for tweet classification BIBREF30, BIBREF27.
Example Output:
What neural classifiers are used?