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: We show that by determining and integrating heterogeneous set of features from different modalities – aesthetic features from posted images (colorfulness, hue variance, sharpness, brightness, blurriness, naturalness), choice of profile picture (for gender, age, and facial expression), the screen name, the language features from both textual content and profile's description (n-gram, emotion, sentiment), and finally sociability from ego-network, and user engagement – we can reliably detect likely depressed individuals in a data set of 8,770 human-annotated Twitter users. 
 Question: What is the source of the user interaction data? 

Output: Sociability from ego-network on Twitter


Input: Consider Input: The corpus is freely available at the following link 
 Question: Is this dataset publicly available?

Output: Yes


Input: Consider Input: To verify the reliability of the transformed semantic space, we propose an evaluation benchmark on the basis of word similarity datasets. Given an enriched space INLINEFORM0 and a similarity dataset INLINEFORM1 , we compute the similarity of each word pair INLINEFORM2 as the cosine similarity of their corresponding transformed vectors INLINEFORM3 and INLINEFORM4 from the two spaces, where INLINEFORM5 and INLINEFORM6 for LS and INLINEFORM7 and INLINEFORM8 for CCA. A high performance on this benchmark shows that the mapping has been successful in placing semantically similar terms near to each other whereas dissimilar terms are relatively far apart in the space. We repeat the computation for each pair in the reverse direction. 
 Question: How is performance measured?


Output: To verify the reliability of the transformed semantic space, we propose an evaluation benchmark on the basis of word similarity datasets. Given an enriched space INLINEFORM0 and a similarity dataset INLINEFORM1 , we compute the similarity of each word pair INLINEFORM2 as the cosine similarity of their corresponding transformed vectors INLINEFORM3 and INLINEFORM4 from the two spaces, where INLINEFORM5 and INLINEFORM6 for LS and INLINEFORM7 and INLINEFORM8 for CCA.