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.

Ex 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?

Ex Output:
Sociability from ego-network on Twitter

Ex Input:
For annotation purposes, we created snippets of conversations as the ones shown in Example 1 and Example 2 consisting of the parent of the suspected trolling event, the suspected trolling event comment, and all of the direct responses to the suspected trolling event. However, the trade off is that snippets like this allow us to make use of Amazon Mechanical Turk (AMT) to have the dataset annotated, because it is not a big burden for a "turker" to work on an individual snippet in exchange for a small pay, and expedites the annotation process by distributing it over dozens of people. Specifically, for each snippet, we requested three annotators to label the four aspects previously described.
Question: how was annotation done?

Ex Output:
Annotation was done with the help of annotators from Amazon Mechanical Turk on snippets of conversations

Ex Input:
We use TB-Dense and MATRES in our experiments and briefly summarize the data statistics in Table TABREF33.
Question: What datasets were used for this work?

Ex Output:
TB-Dense MATRES