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.

Q: Fenerbahce We have decided to consider tweets about popular sports clubs as our domain for stance detection. Hence we have determined our targets as Galatasaray (namely Target-1) and Fenerbahce (namely, Target-2) which are two of the most popular football clubs in Turkey. Then, we have annotated the stance information in the tweets for these targets as Favor or Against. For the purposes of the current study, we have not annotated any tweets with the Neither class.
Question: How were the tweets annotated?

A: tweets are annotated with only Favor or Against for two targets - Galatasaray and Fenerbahce
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Q: The dataset contains a total of 9710 passages, with an average of 6.24 sentences per passage, 16.16 words per sentence, and an average length of 86 words.
Question: What is the size of this dataset?

A: 9710 passages, with an average of 6.24 sentences per passage, 16.16 words per sentence, and an average length of 86 words
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Q: On Twitter we can see results that are consistent with the RCV results for the left-to-center political spectrum. The exception, which clearly stands out, is the right-wing groups ENL and EFDD that seem to be the most cohesive ones. This is the direct opposite of what was observed in the RCV data. We speculate that this phenomenon can be attributed to the fact that European right-wing groups, on a European but also on a national level, rely to a large degree on social media to spread their narratives critical of European integration.
Question: Do the authors mention any possible confounds in their study?

A:
Yes
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