  Social media has become an important data source for event analysis. When
collecting this type of data, most contain no useful information to a target
event. Thus, it is essential to filter out those noisy data at the earliest
opportunity for a human expert to perform further inspection. Most existing
solutions for event filtering rely on fully supervised methods for training.
However, in many real-world scenarios, having access to large number of labeled
samples is not possible. To deal with a few labeled sample training problem for
event filtering, we propose a graph-based few-shot learning pipeline. We also
release the Brazilian Protest Dataset to test our method. To the best of our
knowledge, this dataset is the first of its kind in event filtering that
focuses on protests in multi-modal social media data, with most of the text in
Portuguese. Our experimental results show that our proposed pipeline has
comparable performance with only a few labeled samples (60) compared with a
fully labeled dataset (3100). To facilitate the research community, we make our
dataset and code available at https://github.com/jdnascim/7Set-AL.
