  The collection and examination of social media has become a useful mechanism
for studying the mental activity and behavior tendencies of users. Through the
analysis of collected Twitter data, models were developed for classifying
drug-related tweets. Using topic pertaining keywords, such as slang and methods
of drug consumption, a set of tweets was generated. Potential candidates were
then preprocessed resulting in a dataset of 3,696,150 rows. The classification
power of multiple methods was compared including support vector machines (SVM),
XGBoost, and convolutional neural network (CNN) based classifiers. Rather than
simple feature or attribute analysis, a deep learning approach was implemented
to screen and analyze the tweets' semantic meaning. The two CNN-based
classifiers presented the best result when compared against other
methodologies. The first was trained with 2,661 manually labeled samples, while
the other included synthetically generated tweets culminating in 12,142
samples. The accuracy scores were 76.35% and 82.31%, with an AUC of 0.90 and
0.91. Additionally, association rule mining showed that commonly mentioned
drugs had a level of correspondence with frequently used illicit substances,
proving the practical usefulness of the system. Lastly, the synthetically
generated set provided increased scores, improving the classification
capability and proving the worth of this methodology.
