Semi-supervised Discovery of Informative Tweets During the Emerging Disasters
Table 4: Comparing 4 types of unlabeled corpus.
['[BOLD] Train', '[BOLD] SH  [ITALIC] 3K', '[BOLD] SH  [ITALIC] 50Kp', '[BOLD] SH  [ITALIC] 50K', '[BOLD] SH  [ITALIC] 400M', '[BOLD] BB  [ITALIC] 3K', '[BOLD] BB  [ITALIC] 50Kp', '[BOLD] BB  [ITALIC] 50K', '[BOLD] BB  [ITALIC] 400M']
What is the impact of the unlabeled corpus choice? D50K, D50Kp, D3K, and D400M. On four disasters (SH, OT, AF, QF), the 400 million Twitter corpus results in the best accuracy, it is competitive on WE, and only on BB it is less accurate than D50K. This result is a strong indicator that mantra “the more data the better” works in the emerging disaster scenario. Confirming this, it is interesting to see that using D50K is in general better than using its positively labeled subset D50Kp. Finally, using the location-based tweets from the emerging disaster itself does not seem to be particularly effective, and it could probably be attributed to the small size of this corpus.