  Microblogging platforms such as Twitter are increasingly being used in event
detection. Existing approaches mainly use machine learning models and rely on
event-related keywords to collect the data for model training. These approaches
make strong assumptions on the distribution of the relevant micro-posts
containing the keyword -- referred to as the expectation of the distribution --
and use it as a posterior regularization parameter during model training. Such
approaches are, however, limited as they fail to reliably estimate the
informativeness of a keyword and its expectation for model training. This paper
introduces a Human-AI loop approach to jointly discover informative keywords
for model training while estimating their expectation. Our approach iteratively
leverages the crowd to estimate both keyword specific expectation and the
disagreement between the crowd and the model in order to discover new keywords
that are most beneficial for model training. These keywords and their
expectation not only improve the resulting performance but also make the model
training process more transparent. We empirically demonstrate the merits of our
approach, both in terms of accuracy and interpretability, on multiple
real-world datasets and show that our approach improves the state of the art by
24.3%.
