People increasingly use social media to report emergencies, seek help or
share information during disasters, which makes social networks an important
tool for disaster management. To meet these time-critical needs, we present a
weakly supervised approach for rapidly building high-quality classifiers that
label each individual Twitter message with fine-grained event categories. Most
importantly, we propose a novel method to create high-quality labeled data in a
timely manner that automatically clusters tweets containing an event keyword
and asks a domain expert to disambiguate event word senses and label clusters
quickly. In addition, to process extremely noisy and often rather short
user-generated messages, we enrich tweet representations using preceding
context tweets and reply tweets in building event recognition classifiers. The
evaluation on two hurricanes, Harvey and Florence, shows that using only 1-2
person-hours of human supervision, the rapidly trained weakly supervised
classifiers outperform supervised classifiers trained using more than ten
thousand annotated tweets created in over 50 person-hours.Comment: In Proceedings of the AAAI 2020 (AI for Social Impact Track). Link:
https://aaai.org/ojs/index.php/AAAI/article/view/539