  We tackle the challenge of topic classification of tweets in the context of
analyzing a large collection of curated streams by news outlets and other
organizations to deliver relevant content to users. Our approach is novel in
applying distant supervision based on semi-automatically identifying curated
streams that are topically focused (for example, on politics, entertainment, or
sports). These streams provide a source of labeled data to train topic
classifiers that can then be applied to categorize tweets from more
topically-diffuse streams. Experiments on both noisy labels and human
ground-truth judgments demonstrate that our approach yields good topic
classifiers essentially "for free", and that topic classifiers trained in this
manner are able to dynamically adjust for topic drift as news on Twitter
evolves.
