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Utilizing deep learning and graph mining to identify drug use on Twitter data.

Joseph Tassone1, Peizhi Yan1, Mackenzie Simpson1

  • 1Department of Computer Science, Lakehead University, 955 Oliver Road, Thunder Bay, P7B 5E1, Canada.

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Summary
This summary is machine-generated.

This study developed a model to predict drug-related tweets using social media data. Convolutional Neural Network (CNN) models showed higher accuracy than traditional methods for analyzing drug slang and use-conditions.

Keywords:
BERTBig dataConvolutional neural networkNatural language processingTwitter analysis

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Area of Science:

  • Computational linguistics
  • Social media analytics
  • Public health informatics

Background:

  • Social media platforms like Twitter are valuable for studying user behavior and mental states.
  • Analyzing user-generated content can reveal trends in drug use and related activities.
  • Developing predictive models for drug-related social media content is crucial for public health surveillance.

Purpose of the Study:

  • To develop and compare machine learning models for predicting positively referenced, drug-related tweets.
  • To identify trends and correlations in drug use based on social media data.
  • To assess the effectiveness of different classification methods, including Support Vector Machines (SVM), XGBoost, and Convolutional Neural Networks (CNNs).

Main Methods:

  • Collected and processed a large dataset of Twitter data using drug-related keywords and slang.
  • Compared the predictive performance of SVM, XGBoost, and CNN-based classifiers.
  • Implemented a deep learning approach with CNNs to analyze the semantic meaning of tweets.
  • Utilized both manually labeled and synthetically generated datasets for training CNN models.

Main Results:

  • SVM and XGBoost models showed low predictive capability (accuracy 59.33% and 54.90%, respectively).
  • CNN-based classifiers significantly outperformed traditional models, achieving accuracies of 76.35% and 82.31%.
  • Association rule mining with CNNs identified high probabilities for keywords like "smoke," "cocaine," and "marijuana" in drug-positive classifications.
  • Synthetically generated data improved CNN model accuracy.

Conclusions:

  • CNN-based predictive analysis is a promising approach for analyzing social media text data related to drug use.
  • Attribute-based models are less suitable for analyzing the nuances of textual social media data.
  • The study confirms a correspondence between commonly mentioned drugs on social media and frequently used illicit substances, demonstrating practical utility.
  • Using synthetically generated data enhances model accuracy and predictive power.