  As the problem of drug abuse intensifies in the U.S., many studies that
primarily utilize social media data, such as postings on Twitter, to study drug
abuse-related activities use machine learning as a powerful tool for text
classification and filtering. However, given the wide range of topics of
Twitter users, tweets related to drug abuse are rare in most of the datasets.
This imbalanced data remains a major issue in building effective tweet
classifiers, and is especially obvious for studies that include abuse-related
slang terms. In this study, we approach this problem by designing an ensemble
deep learning model that leverages both word-level and character-level features
to classify abuse-related tweets. Experiments are reported on a Twitter
dataset, where we can configure the percentages of the two classes (abuse vs.
non abuse) to simulate the data imbalance with different amplitudes. Results
show that our ensemble deep learning models exhibit better performance than
ensembles of traditional machine learning models, especially on heavily
imbalanced datasets.
