DeCrisisMB: Debiased Semi-Supervised Learning for Crisis Tweet
  Classification via Memory Bank
Henry Peng Zou Yue Zhou Weizhi Zhang Cornelia Caragea
During crisis events, people often use social media platforms such as Twitter
to disseminate information about the situation, warnings, advice, and support.
Emergency relief organizations leverage such information to acquire timely
crisis circumstances and expedite rescue operations. While existing works
utilize such information to build models for crisis event analysis,
fully-supervised approaches require annotating vast amounts of data and are
impractical due to limited response time. On the other hand, semi-supervised
models can be biased, performing moderately well for certain classes while
performing extremely poorly for others, resulting in substantially negative
effects on disaster monitoring and rescue. In this paper, we first study two
recent debiasing methods on semi-supervised crisis tweet classification. Then
we propose a simple but effective debiasing method, DeCrisisMB, that utilizes a
Memory Bank to store and perform equal sampling for generated pseudo-labels
from each class at each training iteration. Extensive experiments are conducted
to compare different debiasing methods' performance and generalization ability
in both in-distribution and out-of-distribution settings. The results
demonstrate the superior performance of our proposed method. Our code is
available at https://github.com/HenryPengZou/DeCrisisMB.