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.

Input: Consider Input: The dataset consists of more than $0.3$ million records and has been made available for future research.
Question: What is the size of the dataset?

Output: $0.3$ million records

Input: Consider Input: Each tweet is annotated as no evidence of depression (e.g., "Citizens fear an economic depression") or evidence of depression (e.g., "depressed over disappointment"). If a tweet is annotated evidence of depression, then it is further annotated with one or more depressive symptoms, for example, depressed mood (e.g., "feeling down in the dumps"), disturbed sleep (e.g., "another restless night"), or fatigue or loss of energy (e.g., "the fatigue is unbearable") BIBREF10 .
Question: How is the dataset annotated?

Output: no evidence of depression depressed mood disturbed sleep fatigue or loss of energy

Input: Consider Input: We compare our approach to two other, the first one uses almost the same tweet data as we use for training, and the second one is the CrowdFlower dataset annotated for emotions. In the first one Wang et al. BIBREF21 downloaded over 5M tweets which included one of 131 emotional hashtags based on Parrott's three-level categorization of emotions in seven categories: joy, sadness, anger, love, fear, thankfulness, surprise.
Question: What data is used in experiments?
Output: Wang et al. CrowdFlower dataset