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This is not a scientific research, so I didn't compute std, t-stats, etc. But I did pull a few hundred tweets from our database and counted how many wrong ones we had. That's where the number comes from. The filtering scheme is very simple: classify only if we're confident. There are many tweets containing "voted", but we only took ones we have a strong confidence and throw away the rest. For a complete set of keywords used for filtering, please feel free to email.

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