  Social media has become an important tool to share information about crisis
events such as natural disasters and mass attacks. Detecting actionable posts
that contain useful information requires rapid analysis of huge volume of data
in real-time. This poses a complex problem due to the large amount of posts
that do not contain any actionable information. Furthermore, the classification
of information in real-time systems requires training on out-of-domain data, as
we do not have any data from a new emerging crisis. Prior work focuses on
models pre-trained on similar event types. However, those models capture
unnecessary event-specific biases, like the location of the event, which affect
the generalizability and performance of the classifiers on new unseen data from
an emerging new event. In our work, we train an adversarial neural model to
remove latent event-specific biases and improve the performance on tweet
importance classification.
