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.

Example Input: For all tasks, we use a TensorFlow implementation. AraNet predicts age, dialect, gender, emotion, irony, and sentiment from social media posts. It delivers state-of-the-art and competitive performance on these tasks and has the advantage of using a unified, simple framework based on the recently-developed BERT model.  
 Question: Did they experiment on all the tasks?
Example Output: Yes

Example Input: For example, it can be used to extract features for usage with other machine learning tools, or to evaluate given features with the existing classifiers or regressors. Figure FIGREF19 demonstrates a simple feature extractor that retrieves the sentence length. 
 Question: Do they show an example of usage for INFODENS?
Example Output: Yes

Example Input: For our study, we used the number of retweets to single-out those that went viral within our sample. Tweets within that subset (viral tweets hereafter) are varied and relate to different topics. We consider that a tweet contains fake news if its text falls within any of the following categories described by Rubin et al. BIBREF7 (see next section for the details of such categories): serious fabrication, large-scale hoaxes, jokes taken at face value, slanted reporting of real facts and stories where the truth is contentious. The dataset BIBREF8 , manually labelled by an expert, has been publicly released and is available to researchers and interested parties. 
 Question: How did they determine fake news tweets?
Example Output:
an expert annotator determined if the tweet fell under a specific category