Finally, we experiment with using tweets from known GW activists/skeptics as well as GW article headlines taken from extreme liberal/conservative news sources as additional training data, inferring labels based on the stance of the Twitter user or news source.
we find that adding these examples yields a lower performance compared to using only the human-annotated data.