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.

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?
Annotation was done with the help of annotators from Amazon Mechanical Turk on snippets of conversations

First, we adapt a dataset extracted from Freebase, i.e., FB24K, which used by BIBREF26. Then, we collect extra entities and relations that from DBpedia which that they should have at least 100 mentions BIBREF7 and they could link to the entities in the FB24K by the sameAs triples. Finally, we build a datasets named as DBP24K. In addition, we build a game datasets from our game knowledge graph, named as Game30K. 
 Question: What three datasets are used to measure performance?
FB24K DBP24K Game30K

In our experiments, we use as input the 2210 tokenized sentences of the Stanford Sentiment Treebank test set BIBREF2 , preprocessing them by lowercasing as was done in BIBREF8 . 
 Question: Which datasets are used for evaluation?

Stanford Sentiment Treebank