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The collection and examination of social media has become a useful mechanismfor studying the mental activity and behavior tendencies of users. Through theanalysis of collected Twitter data, models were developed for classifyingdrug-related tweets. Using topic pertaining keywords, such as slang and methodsof drug consumption, a set of tweets was generated. Potential candidates werethen preprocessed resulting in a dataset of 3,696,150 rows. The classificationpower of multiple methods was compared including support vector machines (SVM),XGBoost, and convolutional neural network (CNN) based classifiers. Rather thansimple feature or attribute analysis, a deep learning approach was implementedto screen and analyze the tweets' semantic meaning. The two CNN-basedclassifiers presented the best result when compared against othermethodologies. The first was trained with 2,661 manually labeled samples, whilethe other included synthetically generated tweets culminating in 12,142samples. The accuracy scores were 76.35% and 82.31%, with an AUC of 0.90 and0.91. Additionally, association rule mining showed that commonly mentioneddrugs had a level of correspondence with frequently used illicit substances,proving the practical usefulness of the system. Lastly, the syntheticallygenerated set provided increased scores, improving the classificationcapability and proving the worth of this methodology.

Utilizing Deep Learning to Identify Drug Use on Twitter Data