TASK DEFINITION: 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.
PROBLEM: This paper used the real-time method to randomly collect 10% of publicly available English tweets using several pre-defined DDEO-related queries (Table TABREF6 ) within a specific time frame.  
 Question: Do they evaluate only on English data?

SOLUTION: Yes

PROBLEM: The results indicate that most performance gains come from words embeddings, style, and morality features. Other features (emotion and sentiment) show lower importance: nevertheless, they still improve the overall system performance (on average 0.35% Macro-F$_1$ improvement) 
 Question: Based on this paper, what is the more predictive set of features to detect fake news?

SOLUTION: words embeddings, style, and morality features

PROBLEM: We experiment with five benchmark attacking methods for texts: FGM, FGVM, DeepFool BIBREF5, HotFlip BIBREF3) and TYC BIBREF4. 
 Question: What are the benchmark attacking methods?

SOLUTION:
FGM, FGVM, DeepFool BIBREF5, HotFlip BIBREF3) and TYC BIBREF4